Vision loss remains a critical global health issue, with conditions such as diabetic macular edema and age-related macular degeneration often detected only after substantial retinal damage. Early retinal fluid accumulation is a key biomarker, and Optical Coherence Tomography (OCT) provides noninvasive, high-resolution retinal imaging essential for diagnosis. Accurate segmentation of retinal structures is crucial, yet recent conventional deep learning approaches often do not provide satisfactory performance because fluid regions occupy a small portion of the image, leading to inefficient computation and reduced accuracy. We propose CLAVIS, a novel plug-and-play pipeline that leverages CLAssification outputs to generate VIsualizations and guide Segmentation, generating more precise maps of the fluid regions. We leverage visualization techniques to target image regions that influenced the classification outcome. Experimental results demonstrate that this guidance strategy improves Dice score by $\mathbf{+ 0. 1 4}$ on the average and up to $\mathbf{+ 0. 1 8}$ for some pathologies.
ICASSP 26
Sparse recovery using tight frames and minimax concave penalty
Sparse signal recovery is an important problem in compressed sensing, signal/image processing, computational imaging and machine learning. The design of the sensing matrix is crucial in compressed sensing. While random Gaussian sensing matrices are relatively easier to construct, it is known that matrices constituting a tight frame provide superior reconstruction performance. We formulate the problem of sparse signal recovery using a new data-fidelity term that effectively "tightens" the sensing matrix, and incorporate the minimax concave penalty (MCP) instead of the ℓ1-norm for promoting sparsity. We carry out the optimization using a proximal gradient method and its Nesterov accelerated counterpart. Simulation results demonstrate that the proposed algorithms result in up to three times faster convergence and superior reconstruction accuracy over benchmark methods.
3D Gaussian Splatting (3DGS) has revolutionized novel view synthesis by offering excellent visual quality, real-time rendering and faster training. However, the infinite support and inherent smoothness of Gaussian kernels limit their ability to reconstruct fine geometric details and high-frequency texture, while also introducing domain-clipping artifacts near scene boundaries. We introduce SplineSplat, a novel approach that replaces Gaussian kernels with compactly supported and scalable polynomial B-spline bases, specifically, linear, quadratic, and cubic splines. B-Splines offer improved spatial localization, greater representational flexibility, and efficient evaluation through piecewise-polynomial definitions. We utilize fast spline computation strategies that enable significant training acceleration without sacrificing rendering quality or speed. Evaluations on the Mip-NeRF 360 static dataset and NVIDIA dynamic dataset demonstrate that SplineSplat matches the reconstruction fidelity of 3DGS while reducing training time by up to 18\%, making it an effective kernel for high-performance radiance-field representations.
@inproceedings{10.1145/3757376.3771418,address={New York, NY, USA},articleno={18},author={Thomas, N. G. and Seelamantula, C. S.},booktitle={Proceedings of the SIGGRAPH Asia 2025 Technical Communications},doi={10.1145/3757376.3771418},isbn={9798400721366},location={},numpages={4},publisher={Association for Computing Machinery},series={SA Technical Communications '25},title={{SplineSplat}: {R}epresenting Radiance Fields Using {B}-Splines},url={https://doi.org/10.1145/3757376.3771418},year={2025}}
@article{bakshi2025lure,author={Bakshi, M. and Venkat, G. and Bisen, N. and Seelamantula, C. S. and Blu, T.},journal={SIAM Journal on Imaging Sciences},number={4},pages={2580--2604},publisher={SIAM},title={LURE: An Unsupervised Denoising Framework for Multiplicative Lognormal Noise},url={https://epubs.siam.org/doi/full/10.1137/24M1700508},volume={18},year={2025}}
@inproceedings{gupta2025deep,author={Gupta, A. and Seelamantula, C. S. and Blu, T. and Dube, N. and Raman, S.},booktitle={IEEE International Conference on Image Processing (ICIP)},doi={10.1109/ICIP55913.2025.11084619},number={},pages={893-898},title={Deep Unsupervised Despeckling With Unbiased Risk Estimation},url={https://ieeexplore.ieee.org/document/11084619},volume={},year={2025}}
@misc{jha2025image,archiveprefix={arXiv},author={Jha, A. and Dhanireddy, C. and Seelamantula, C. S. and Thakur, C. S.},eprint={2509.05977},primaryclass={eess.SP},title={3D-Image Reconstruction using MIMO-SAR FMCW Radar},url={https://arxiv.org/abs/2509.05977},year={2025}}
@misc{prabakar2025weakly,archiveprefix={arXiv},author={Prabakar, A. and Bhandiwad, A. S. and Kamath, A. J. and Seelamantula, C. S.},eprint={2508.14438},primaryclass={eess.SP},title={Weakly-Convex Regularization for Magnetic Resonance Image Denoising},url={https://arxiv.org/abs/2508.14438},year={2025}}
@article{kamath2025deepfri,author={Kamath, A. J. and Patil, S. B. and Seelamantula, C. S.},doi={10.1109/TSP.2025.3589394},journal={IEEE Transactions on Signal Processing},title={{DeepFRI:} A Deep Plug-and-Play Technique for Finite-Rate-of-Innovation Signal Reconstruction},url={https://ieeexplore.ieee.org/abstract/document/11080367},year={2025}}
@inproceedings{kamath2025neuromorphic,article_page={/articles/neuromorphic-unlimited-sampling/},author={Kamath, A. J. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Neuromorphic Unlimited Sampling for High-Dynamic-Range Video Acquisition},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:BJbdYPG6LGMC},year={2025}}
@inproceedings{verma2025diffusion,author={Verma, A. and Boominathan, V. and Veeraraghavan, A. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Diffusion Model Based Image Reconstruction in Lensless Imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:vDZJ-YLwNdEC},year={2025}}
@inproceedings{kamath2025design,author={Kamath, A. J. and Bhandiwad, A. S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={On the Design of Weakly-Convex Regularizers for Solving Linear Inverse Problems},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:artPoR2Yc-kC},year={2025}}
@inproceedings{shetty2025monte,author={Shetty, N. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Monte Carlo Score Matching for Image Generation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:tH6gc1N1XXoC},year={2025}}
@inproceedings{nareddy2025intriguing,author={Nareddy, K. K. R. and Perumal, I. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Some Intriguing Observations on the Learnt Matrices in Deep Unfolded Networks},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:gKiMpY-AVTkC},year={2025}}
@inproceedings{bakshi2025efficientnet,author={Bakshi, M. and Seelamantula, C. S.},booktitle={MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy},title={EfficientNet-B1 Based Diabetic Retinopathy Detection from Ultra-widefield Fundus Images},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Ak0FvsSvgGUC},year={2025}}
@inproceedings{bharadwaj2025obscure,author={Bharadwaj, S. and Seth, P. and Seelamantula, C. S.},booktitle={Medical Imaging with Deep Learning-Short Papers},title={Obscure to Observe: A Lesion-Aware MAE for Glaucoma Detection from Retinal Context},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Bg7qf7VwUHIC},year={2025}}
@article{nareddy2024tight,article_page={/articles/tight-frame-data-fidelity/},author={Nareddy, K. K. R. and Kamath, A. J. and Seelamantula, C. S.},journal={SIAM Journal on Imaging Sciences},number={3},pages={1587--1618},title={Tight-Frame-Like Analysis-Sparse Recovery Using Nontight Sensing Matrices},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:5MTHONV0fEkC},volume={17},year={2024}}
@inproceedings{kamath2024neuromorphic,author={Kamath, A. J. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Neuromorphic Sensing Meets Unlimited Sampling},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_OXeSy2IsFwC},year={2024}}
@inproceedings{bhandiwad2024variational,author={Bhandiwad, A. S. and Kamath, A. J. and Asokan, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Variational Analysis of Adversarial Regularization for Solving Inverse Problems},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:GFxP56DSvIMC},year={2024}}
@inproceedings{nareddy2024image,author={Nareddy, K. K. R. and Kamath, A. J. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Image Restoration with Generalized L2 Loss and Convergent Plug-and-Play Priors},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:8xutWZnSdmoC},year={2024}}
@inproceedings{herur2024addressing,author={Herur, A. N. and Santhosh, V. and Shetty, N. and Seelamantula, C. S.},booktitle={Indian Conference on Computer Vision Graphics and Image Processing (ICVGIP)},title={Addressing Diffusion Model Based Counter-Forensic Image Manipulation for Synthetic Image Detection},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:nVrZBo8bIpAC},year={2024}}
@inproceedings{kamath2024method,author={Kamath, A. J. and Reddy, N. K. K. and Seelamantula, C. S.},booktitle={International Conference on Signal Processing and Communications (SPCOM)},title={Method of Alternating Proximations for Solving Linear Inverse Problems},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:lvd772isFD0C},year={2024}}
@inproceedings{shetty2024momentum,author={Shetty, N. and Bandla, M. and Neema, N. and Asokan, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Momentum-Imbued Langevin Dynamics (MILD) for Faster Sampling},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ZzlSgRqYykMC},year={2024}}
@article{kumar2023chaksu,author={Kumar, J. R. H. and Seelamantula, C. S. and Gagan, J. H. and Kamath, Y. S. and Kuzhuppilly, N. I. R. and others},journal={Scientific data},number={1},pages={70},title={Ch{\'a}k{\d{s}}u: A glaucoma specific fundus image database},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:HtS1dXgVpQUC},volume={10},year={2023}}
@article{mangalwedhekar2023achieving,author={Mangalwedhekar, R. and Singh, N. and Thakur, C. S. and Seelamantula, C. S. and Jose, M. and others},journal={Nature Nanotechnology},number={4},pages={380--389},title={Achieving nanoscale precision using neuromorphic localization microscopy},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:DJbcl8HfkQkC},volume={18},year={2023}}
@article{mache2023introducing,author={Mache, S. and Pokala, P. K. and Rajendran, K. and Seelamantula, C. S.},journal={IEEE Transactions on Computational Imaging},pages={475--489},title={Introducing nonuniform sparse proximal averaging network for seismic reflectivity inversion},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:HeT0ZceujKMC},volume={9},year={2023}}
@article{asokan2023euler,author={Asokan, S. and Seelamantula, C. S.},journal={Journal of Machine Learning Research},number={126},pages={1--100},title={Euler-Lagrange analysis of generative adversarial networks},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:yMeIxYmEMEAC},volume={24},year={2023}}
@article{kamath2023neuromorphic,author={Kamath, A. J. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2306.05103},title={Neuromorphic sampling of signals in shift-invariant spaces},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:a3BOlSfXSfwC},year={2023}}
@article{asokan2023data,author={Asokan, S. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2306.00785},title={Data Interpolants--That's What Discriminators in Higher-order Gradient-regularized GANs Are},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:r_AWSJRzSzQC},year={2023}}
CVPR 23
Spider GAN: Leveraging friendly neighbors to accelerate GAN training
@inproceedings{asokan2023spider,author={Asokan, S. and Seelamantula, C. S.},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},title={Spider GAN: Leveraging friendly neighbors to accelerate GAN training},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:fFSKOagxvKUC},year={2023}}
@inproceedings{kamath2023multichannel,author={Kamath, A. J. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Multichannel time-encoding of finite-rate-of-innovation signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:3htObqc8RwsC},year={2023}}
@article{asokan2023gans,author={Asokan, S. and Shetty, N. and Srikanth, A. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2306.01654},title={Gans settle scores!},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:LhH-TYMQEocC},year={2023}}
@article{kamath2023neuromorphic_sparse,author={Kamath, A. J. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2310.15750},title={Neuromorphic Sampling of Sparse Signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:nZcligLrVowC},year={2023}}
@article{kasireddy2023deep,author={Kasireddy, H. R. and Kallam, U. R. and Mantrala, S. K. S. and Kongara, H. and Shivhare, A. and Saita, J. and others},journal={Diagnostics},number={16},pages={2659},title={Deep-learning-based visualization and volumetric analysis of fluid regions in optical coherence tomography scans},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:OP4eGU-M3BUC},volume={13},year={2023}}
CVPR 23
Quantized proximal averaging networks for compressed image recovery
@inproceedings{reddy2023quantized,author={Reddy, N. K. K. and Bulusu, M. M. and Pokala, P. K. and Seelamantula, C. S.},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},title={Quantized proximal averaging networks for compressed image recovery},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:yqoGN6RLRZoC},year={2023}}
@inproceedings{asokan2023game,author={Asokan, S. and Mohammed, F. S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={A Game of Snakes and Gans},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:OR75R8vi5nAC},year={2023}}
@article{kumar2023chaksu_erratum,author={Kumar, J. R. and Seelamantula, C. S. and Gagan, J. H. and Kamath, Y. S. S. and Kuzhuppilly, N. I. R. and others},journal={Scientific Data},number={1},title={Chaksu: A glaucoma specific fundus image database (vol 10, 70, 2023)},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Aul-kAQHnToC},volume={10},year={2023}}
ICCV 23
Quantized Generative Models for Solving Inverse Problems
@inproceedings{reddy2023quantized_generative,author={Reddy, N. K. K. and Killedar, V. and Seelamantula, C. S.},booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},title={Quantized Generative Models for Solving Inverse Problems},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:P7Ujq4OLJYoC},year={2023}}
@inproceedings{killedar2022compressive,author={Killedar, V. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Compressive phase retrieval based on sparse latent generative priors},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:9c2xU6iGI7YC},year={2022}}
@inproceedings{kamath2022differentiate,author={Kamath, A. J. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Differentiate-and-fire time-encoding of finite-rate-of-innovation signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:IUKN3-7HHlwC},year={2022}}
@article{mukherjee2022quantization,author={Mukherjee, S. and Seelamantula, C. S.},journal={International Journal of Wavelets, Multiresolution and Information Processing},title={Quantization-aware phase retrieval},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:KUbvn5osdkgC},year={2022}}
@inproceedings{asokan2022lsgans,author={Asokan, S. and Seelamantula, C. S.},booktitle={First Workshop on Interpolation Regularizers and Beyond at NeurIPS},title={LSGANs with gradient regularizers are smooth high-dimensional interpolators},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:mNrWkgRL2YcC},year={2022}}
@article{mache2022hilbert,author={Mache, S. and Chatterjee, A. and Rajendran, K. and Seelamantula, C. S.},journal={Bulletin of the Seismological Society of America},number={6},pages={2847--2858},title={Hilbert–Huang Transform and Energy Rate Functions for Earthquake Source Characterization—A Study from the Japan Trench},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:HIFyuExEbWQC},volume={112},year={2022}}
@inproceedings{nareddy2022ensemble,author={Nareddy, K. K. R. and Mache, S. and Pokala, P. K. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={1251--1255},title={An ensemble of proximal networks for sparse coding},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:4vMrXwiscB8C},year={2022}}
@inproceedings{asokan2022bridging,author={Asokan, S. and Seelamantula, C. S.},booktitle={The Symbiosis of Deep Learning and Differential Equations II},title={Bridging the gap between Coulomb GAN and gradient-regularized WGAN},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:cWzG1nlazyYC},year={2022}}
@article{gagan2022rapid,author={Gagan, J. H. and Kumar, J. R. H. and Seelamantula, C. S. and Adiga, C. S.},journal={ISSS Journal of Micro and Smart Systems},number={2},pages={397--405},title={RaPiD: a Raspberry Pi-based optical fundoscope},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:dBIO0h50nwkC},volume={11},year={2022}}
@article{john2021adaptive,author={John, A. and Sadasivan, J. and Seelamantula, C. S.},journal={IEEE Transactions on Signal Processing},pages={5021--5036},title={Adaptive Savitzky-Golay filtering in non-Gaussian noise},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_5tno0g5mFcC},volume={69},year={2021}}
@article{pokala2021iteratively,author={Pokala, P. K. and Hemadri, R. V. and Seelamantula, C. S.},journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},number={12},pages={8992--9005},title={Iteratively reweighted minimax-concave penalty minimization for accurate low-rank plus sparse matrix decomposition},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:WJVC3Jt7v1AC},volume={44},year={2021}}
@article{mache2021durin,author={Mache, S. and Pokala, P. K. and Rajendran, K. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2104.04704},title={DuRIN: A deep-unfolded sparse seismic reflectivity inversion network},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:HtEfBTGE9r8C},year={2021}}
@article{kamath2021time,author={Kamath, A. J. and Rudresh, S. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2107.03344},title={Time encoding of finite-rate-of-innovation signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:DUooU5lO8OsC},year={2021}}
@article{mache2021nuspan,author={Mache, S. and Pokala, P. K. and Rajendran, K. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2105.00003},title={NuSPAN: A Proximal Average Network for Nonuniform Sparse Model--Application to Seismic Reflectivity Inversion},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:a9-T7VOCCH8C},year={2021}}
@inproceedings{killedar2021sparsity,author={Killedar, V. and Pokala, P. K. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Sparsity driven latent space sampling for generative prior based compressive sensing},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:4hFrxpcac9AC},year={2021}}
@article{killedar2021learning,author={Killedar, V. and Pokala, P. K. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2105.11956},title={Learning generative prior with latent space sparsity constraints},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:umqufdRvDiIC},year={2021}}
@article{varghese2021image,author={Varghese, R. and Seelamantula, C. and Gupta, A. and Dhar, D.},journal={arXiv preprint arXiv:2111.08297},title={Image denoising in FPGA using generic risk estimation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:bKqednn6t2AC},year={2021}}
@article{nareddy2021quantized_proximal,author={Nareddy, K. K. R. and Bulusu, M. M. and Pokala, P. K. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2105.06211},title={Quantized proximal averaging network for analysis sparse coding},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:XvxMoLDsR5gC},year={2021}}
@inproceedings{mache2021explainable,author={Mache, S. and Pokala, P. K. and Rajendran, K. and Seelamantula, C. S.},booktitle={AGU Fall Meeting Abstracts},pages={S15F-0312},title={Explainable Deep Neural Networks for Seismic Reflectivity Inversion},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:IRz6iEL74y4C},volume={2021},year={2021}}
@article{jawali2021wavelet,author={Jawali, D. and Kumar, A. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2107.11225},title={Wavelet Design in a Learning Framework},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:6ZxmRoH8BuwC},year={2021}}
@inproceedings{rudresh2020time,author={Rudresh, S. and Kamath, A. J. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={A time-based sampling framework for finite-rate-of-innovation signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:edDO8Oi4QzsC},year={2020}}
@article{mangalore2020neuromorphic,author={Mangalore, A. R. and Seelamantula, C. S. and Thakur, C. S.},journal={IEEE Signal Processing Letters},pages={1510--1514},title={Neuromorphic fringe projection profilometry},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:bz8QjSJIRt4C},volume={27},year={2020}}
@article{asokan2020teaching,author={Asokan, S. and Seelamantula, C.},journal={Advances in Neural Information Processing Systems},pages={3964--3975},title={Teaching a gan what not to learn},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:LO7wyVUgiFcC},volume={33},year={2020}}
@article{shastri2020axial,author={Shastri, S. K. and Rudresh, S. and Anand, R. and Nagesh, S. and Seelamantula, C. S. and Thittai, A. K.},journal={Ultrasonics},pages={106183},title={Axial super-resolution in ultrasound imaging with application to non-destructive evaluation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:YohjEiUPhakC},volume={108},year={2020}}
@inproceedings{raj2020automatic,author={Raj, P. K. and Manjunath, A. and Kumar, J. R. H. and Seelamantula, C. S.},booktitle={IEEE 17th International Symposium on Biomedical Imaging (ISBI)},pages={1262--1265},title={Automatic classification of artery/vein from single wavelength fundus images},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:PVjk1bu6vJQC},year={2020}}
@inproceedings{paul2020fully,author={Paul, S. and Gundabattula, H. D. and Seelamantula, C. S. and Mujeeb, V. R. and Prasad, A. S.},booktitle={IEEE 17th International Symposium on Biomedical Imaging (ISBI)},pages={221--224},title={Fully-automated semantic segmentation of wireless capsule endoscopy abnormalities},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:5qfkUJPXOUwC},year={2020}}
@article{sadasivan2020speech,author={Sadasivan, J. and Seelamantula, C. S. and Muraka, N. R.},journal={Speech Communication},pages={12--29},title={Speech enhancement using a risk estimation approach},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ruyezt5ZtCIC},volume={116},year={2020}}
@inproceedings{pokala2020accelerated,author={Pokala, P. K. and Seelamantula, C. S.},booktitle={International Conference on Signal Processing and Communications (SPCOM)},title={Accelerated Weighted ℓ1-Minimization for MRI Reconstruction Under Tight Frames in Complex Domain},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Ri6SYOTghG4C},year={2020}}
@inproceedings{pokala2020confirmnet,author={Pokala, P. K. and Uttam, P. K. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={ConFirmNet: Convolutional FirmNet and application to image denoising and inpainting},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:0N-VGjzr574C},year={2020}}
@article{manjunath2020robust,author={Manjunath, A. and Jois, S. and Seelamantula, C. S.},journal={arXiv preprint arXiv:2012.07128},title={Robust segmentation of optic disc and cup from fundus images using deep neural networks},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:anf4URPfarAC},year={2020}}
@inproceedings{jawali2020cornet,author={Jawali, D. and Pokala, P. K. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={818--822},title={CoRNet: Composite-regularized neural network for convolutional sparse coding},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:FAceZFleit8C},year={2020}}
@article{kumar2020automatic,author={Kumar, J. R. H. and Seelamantula, C. S. and Mohan, A. and Shetty, R. and Berendschot, T. J. M. and others},journal={Plos one},number={5},pages={e0231677},title={Automatic analysis of normative retinal oximetry images},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:hCrLmN-GePgC},volume={15},year={2020}}
@inproceedings{kishore2020wirtinger,author={Kishore, V. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Wirtinger flow algorithms for phase retrieval from binary measurements},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ClCfbGk0d_YC},year={2020}}
@article{sadasivan2020musical,author={Sadasivan, J. and Dhiman, J. K. and Seelamantula, C. S.},journal={Speech Communication},pages={41--52},title={Musical noise suppression using a low-rank and sparse matrix decomposition approach},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:4fGpz3EwCPoC},volume={125},year={2020}}
@inproceedings{srinath2020nyquist,author={Srinath, S. and Rudresh, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={2965--2969},title={Nyquist pulses for sub-Nyquist sampling—Application to underwater imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:8d8msizDQcsC},year={2020}}
@inproceedings{pokala2020projected,author={Pokala, P. K. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={1043--1047},title={Projected improved fista and application to image deblurring},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:9pM33mqn1YgC},year={2020}}
@inproceedings{kishore2020phasesense,author={Kishore, V. and Mukherjee, S. and Seelamantula, C. S.},booktitle={International Conference on Signal Processing and Communications (SPCOM)},title={PhaseSense—Signal Reconstruction from Phase-Only Measurements via Quadratic Programming},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:kh2fBNsKQNwC},year={2020}}
@article{sankar2020digitization,author={Sankar S, H. and Patni, A. and Mulleti, S. and Seelamantula, C. S.},journal={bioRxiv},title={Digitization of electrocardiogram using bilateral filtering},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:rmuvC79q63oC},year={2020}}
@article{sadasivan2020signal,author={Sadasivan, J. and Mukherjee, S. and Seelamantula, C. S.},journal={APSIPA Transactions on Signal and Information Processing},pages={e3},title={Signal denoising using the minimum-probability-of-error criterion},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Fu2w8maKXqMC},volume={9},year={2020}}
@inproceedings{pokala2020adaptive,author={Pokala, P. K. and Chemudupati, S. and Seelamantula, C. S.},booktitle={IEEE 17th International Symposium on Biomedical Imaging (ISBI)},pages={1929--1932},title={Adaptive weighted minimax-concave penalty based dictionary learning for brain MR images},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:86PQX7AUzd4C},year={2020}}
@inproceedings{pokala2020generalized,author={Pokala, P. K. and Chemudupati, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={2875--2879},title={Generalized fast iteratively reweighted soft-thresholding algorithm for sparse coding under tight frames in the complex-domain},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_FM0Bhl9EiAC},year={2020}}
@inproceedings{kulkarni2020epoch,author={Kulkarni, P. and Sadasivan, J. and Adiga, A. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Epoch Estimation from a Speech Signal Using Gammatone Wavelets in a Scattering Network},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:FPJr55Dyh1AC},year={2020}}
@inproceedings{chemudupati2020non,author={Chemudupati, S. and Pokala, P. K. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={2885--2889},title={Non-Convex Optimization For Sparse Interferometric Phase Estimation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:GtLg2Ama23sC},year={2020}}
@article{mahurkar2020minkowski,author={Mahurkar, A. G. and Seelamantula, C. S.},journal={IEEE Signal Processing Letters},pages={1060--1064},title={Minkowski-Algebra-Based Super-Sparse Array Design for Super-Resolution Ultrasound Imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_axFR9aDTf0C},volume={27},year={2020}}
@article{kumar2019rim,author={Kumar, J. R. H. and Seelamantula, C. S. and Kamath, Y. S. and Jampala, R.},journal={Scientific reports},number={1},pages={7099},title={Rim-to-disc ratio outperforms cup-to-disc ratio for glaucoma prescreening},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:WZBGuue-350C},volume={9},year={2019}}
@inproceedings{sadasivan2019high,author={Sadasivan, V. S. and Seelamantula, C. S.},booktitle={IEEE 16th International Symposium on Biomedical Imaging (ISBI)},pages={96--99},title={High accuracy patch-level classification of wireless capsule endoscopy images using a convolutional neural network},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:i2xiXl-TujoC},year={2019}}
@inproceedings{mohan2019optic,author={Mohan, D. and Kumar, J. R. H. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={834--838},title={Optic disc segmentation using cascaded multiresolution convolutional neural networks},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:SpbeaW3--B0C},year={2019}}
@article{krishna2019unlimited,author={Krishna, A. and Rudresh, S. and Shaw, V. and Sabbella, H. R. and Seelamantula, C. S. and others},journal={arXiv preprint arXiv:1911.09371},title={Unlimited dynamic range analog-to-digital conversion},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:XoXfffV-tXoC},year={2019}}
@inproceedings{pokala2019firmnet,author={Pokala, P. K. and Mahurkar, A. G. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={FirmNet: A sparsity amplified deep network for solving linear inverse problems},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:LI9QrySNdTsC},year={2019}}
@inproceedings{jawali2019learning,author={Jawali, D. and Kumar, A. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={A learning approach for wavelet design},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:kz9GbA2Ns4gC},year={2019}}
@inproceedings{raj2019structure,author={Raj, P. K. and Kumar, J. R. H. and Jois, S. and Harsha, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={829--833},title={A structure tensor based Voronoi decomposition technique for optic cup segmentation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:PoWvk5oyLR8C},year={2019}}
@inproceedings{dey2019automatic,author={Dey, S. and Tahiliani, K. and Kumar, J. R. H. and Pediredla, A. K. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Automatic segmentation of optic disc using affine snakes in gradient vector field},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:lmc2jWPfTJgC},year={2019}}
@inproceedings{kumar2019automatic,author={Kumar, J. R. H. and Teotia, K. and Raj, P. K. and Andrade, J. and Rajagopal, K. V. and others},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Automatic segmentation of common carotid artery in longitudinal mode ultrasound images using active oblongs},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:k8Z6L05lTy4C},year={2019}}
@inproceedings{gupta2019portable,author={Gupta, S. K. and Kumar, K. and Seelamantula, C. S. and Thakur, C. S.},booktitle={41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},title={A portable ultrasound imaging system utilizing deep generative learning-based compressive sensing on pre-beamformed RF signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:HbR8gkJAVGIC},year={2019}}
@inproceedings{kamath2019fri,author={Kamath, A. J. and Rudresh, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={FRI Modelling of Fourier Descriptors},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:M7yex6snE4oC},year={2019}}
@inproceedings{mahurkar2019samir,author={Mahurkar, A. G. and Pokala, P. K. and Thakur, C. S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={SAMIR: Sparsity amplified iteratively-reweighted beamforming for high-resolution ultrasound imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:MLfJN-KU85MC},year={2019}}
@inproceedings{dhiman2019spectro,author={Dhiman, J. K. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={A Spectro-temporal Technique for Estimating Aperiodicity and Voiced/unvoiced Decision Boundaries of Speech Signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:VaXvl8Fpj5cC},year={2019}}
@inproceedings{mahurkar2019iteratively,author={Mahurkar, A. G. and Pokala, P. and Seelamantula, C. S.},booktitle={IEEE 16th International Symposium on Biomedical Imaging (ISBI)},title={Iteratively-reweighted beamforming for high-resolution ultrasound imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:S16KYo8Pm5AC},year={2019}}
@inproceedings{shastri2019axial,author={Shastri, S. K. and Rudresh, S. and Anand, R. and Nagesh, S. and Mazumder, D. and Thittai, A. K. and others},booktitle={IEEE International Ultrasonics Symposium (IUS)},pages={639--642},title={Axial Super-Resolution in Ultrasound Imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Ug5p-4gJ2f0C},year={2019}}
@article{shastri2019generalized,author={Shastri, S. K. and Rudresh, S. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1902.02732},title={Generalized Design of Sampling Kernels for 2-D FRI Signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:gsN89kCJA0AC},year={2019}}
@inproceedings{dhiman2019suitability,author={Dhiman, J. K. and Adiga, N. and Seelamantula, C. S.},booktitle={INTERSPEECH},pages={944--948},title={On the Suitability of the Riesz Spectro-Temporal Envelope for WaveNet Based Speech Synthesis},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Dip1O2bNi0gC},year={2019}}
@inproceedings{rudresh2018wavelet,author={Rudresh, S. and Adiga, A. and Shenoy, B. A. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Wavelet-based reconstruction for unlimited sampling},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ML0RJ9NH7IQC},year={2018}}
@article{sundar2018tdoa,author={Sundar, H. and Sreenivas, T. V. and Seelamantula, C. S.},journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},number={11},pages={1976--1990},title={TDOA-based multiple acoustic source localization without association ambiguity},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:BwyfMAYsbu0C},volume={26},year={2018}}
@inproceedings{mohan2018high,author={Mohan, D. and Kumar, J. R. H. and Seelamantula, C. S.},booktitle={25th IEEE international conference on image processing (ICIP)},pages={4038--4042},title={High-performance optic disc segmentation using convolutional neural networks},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:vbGhcppDl1QC},year={2018}}
@article{rudresh2018epoch,author={Rudresh, S. and Vasisht, A. and Vijayan, K. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1801.06492},title={Epoch-synchronous overlap-add (ESOLA) for time-and pitch-scale modification of speech signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:uWiczbcajpAC},year={2018}}
@article{chandran2018duration,author={Chandran KS, S. and Seelamantula, C. S. and Ray, S.},journal={Journal of neurophysiology},number={3},pages={808--821},title={Duration analysis using matching pursuit algorithm reveals longer bouts of gamma rhythm},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Z5m8FVwuT1cC},volume={119},year={2018}}
@article{rudresh2018asymmetric,author={Rudresh, S. and Nagesh, S. and Seelamantula, C. S.},journal={IEEE Transactions on Signal Processing},number={8},pages={2027--2040},title={Asymmetric pulse modeling for FRI sampling},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:BUYA1_V_uYcC},volume={66},year={2018}}
@article{mukherjee2018phase,author={Mukherjee, S. and Seelamantula, C. S.},journal={IEEE Signal Processing Letters},number={3},pages={348--352},title={Phase retrieval from binary measurements},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:z_wVstp3MssC},volume={25},year={2018}}
@article{shenoy2018two,author={Shenoy, B. A. and Mulleti, S. and Seelamantula, C. S.},journal={IEEE Transactions on Signal Processing},number={14},pages={3906--3917},title={On two-dimensional Hilbert integral equations, generalized minimum-phase signals, and phase retrieval},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:4MWp96NkSFoC},volume={66},year={2018}}
@inproceedings{sridhar2018unconstrained,author={Sridhar, H. and Kumar, J. R. H. and Jois, S. and Seelamantula, C. S.},booktitle={IEEE Global Conference on Signal and Information Processing (GlobalSIP)},title={An unconstrained ellipse fitting technique and application to optic cup segmentation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:TIZ-Mc8IlK0C},year={2018}}
@inproceedings{kumar2018automatic_lumen,author={Kumar, J. R. H. and Seelamantula, C. S. and Andrade, J. and Rajagopal, K. V.},booktitle={25th IEEE International Conference on Image Processing (ICIP)},pages={3493--3497},title={Automatic segmentation of lumen intima layer in transverse mode ultrasound images},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:p__nRnzSRKYC},year={2018}}
@inproceedings{mukherjee2018phasesplit,author={Mukherjee, S. and Shit, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Phasesplit: A variable splitting framework for phase retrieval},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:g3aElNc5_aQC},year={2018}}
@inproceedings{sadasivan2018speech,author={Sadasivan, J. and Mukherjee, S. and Seelamantula, C. S.},booktitle={INTERSPEECH},pages={1141--1145},title={Speech Enhancement Using the Minimum-probability-of-error Criterion},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:EYYDruWGBe4C},year={2018}}
@inproceedings{sainathan2018phase,author={Sainathan, A. and Seelamantula, C. S.},booktitle={Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)},title={Phase Retrieval–A Deconvolution Perspective},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:tuHXwOkdijsC},year={2018}}
@inproceedings{de2018design,author={De, A. and Seelamantula, C. S.},booktitle={25th IEEE International Conference on Image Processing (ICIP)},pages={1443--1447},title={Design of Sampling Kernels and Sampling Rates for Two-Dimensional Finite Rate of Innovation Signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:hMsQuOkrut0C},year={2018}}
SPCOM 18
Binary compressive sensing and super-resolution with unknown threshold
@inproceedings{mukherjee2018binary,author={Mukherjee, S. and Sekuboyina, A. K. and Seelamantula, C. S.},booktitle={International Conference on Signal Processing and Communications (SPCOM)},title={Binary compressive sensing and super-resolution with unknown threshold},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:-FonjvnnhkoC},year={2018}}
SPCOM 18
A singular value relaxation technique for learning sparsifying transforms
@inproceedings{mukherjee2018singular,author={Mukherjee, S. and Seelamantula, C. S.},booktitle={International Conference on Signal Processing and Communications (SPCOM)},title={A singular value relaxation technique for learning sparsifying transforms},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:e_rmSamDkqQC},year={2018}}
@inproceedings{dhiman2018multicomponent,author={Dhiman, J. K. and Sharma, N. and Seelamantula, C. S.},booktitle={INTERSPEECH},pages={736--740},title={Multicomponent 2-D AM-FM Modeling of Speech Spectrograms},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ILKRHgRFtOwC},year={2018}}
@inproceedings{sainathan2018optimization,author={Sainathan, A. and Rudresh, S. and Seelamantula, C. S.},booktitle={INTERSPEECH},pages={741--745},title={An Optimization Framework for Recovery of Speech from Phase-Encoded Spectrograms},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:L7CI7m0gUJcC},year={2018}}
@inproceedings{sekuboyina2017convolutional,author={Sekuboyina, A. K. and Devarakonda, S. T. and Seelamantula, C. S.},booktitle={IEEE 14th international symposium on biomedical imaging (ISBI)},title={A convolutional neural network approach for abnormality detection in wireless capsule endoscopy},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_Re3VWB3Y0AC},year={2017}}
@article{mulleti2017paley,author={Mulleti, S. and Seelamantula, C. S.},journal={IEEE Transactions on Signal Processing},number={22},pages={5860--5872},title={Paley–Wiener characterization of kernels for finite-rate-of-innovation sampling},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:t7zJ5fGR-2UC},volume={65},year={2017}}
@article{rudresh2017finite,author={Rudresh, S. and Seelamantula, C. S.},journal={IEEE Transactions on Signal Processing},number={19},pages={5021--5033},title={Finite-rate-of-innovation-sampling-based super-resolution radar imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:VLnqNzywnoUC},volume={65},year={2017}}
@article{bhowmik2017training,author={Bhowmik, A. and Shit, S. and Seelamantula, C. S.},journal={IEEE signal processing letters},number={1},pages={85--89},title={Training-free, single-image super-resolution using a dynamic convolutional network},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:0KyAp5RtaNEC},volume={25},year={2017}}
@article{mahapatra2017deep,author={Mahapatra, D. and Mukherjee, S. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1705.07290},title={Deep sparse coding using optimized linear expansion of thresholds},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ZfRJV9d4-WMC},year={2017}}
@article{mulleti2017super,author={Mulleti, S. and Singh, A. and Brahmkhatri, V. P. and Chandra, K. and Raza, T. and Mukherjee, S. P. and others},journal={Scientific reports},number={1},pages={9651},title={Super-resolved nuclear magnetic resonance spectroscopy},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:9Nmd_mFXekcC},volume={7},year={2017}}
@article{chaturvedi2017efficient,author={Chaturvedi, A. and Nagaraj, S. K. and Gorthi, S. S. and Seelamantula, C. S.},journal={SLAS TECHNOLOGY: Translating Life Sciences Innovation},number={5},pages={565--572},title={An efficient microscale technique for determining the erythrocyte sedimentation rate},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:7T2F9Uy0os0C},volume={22},year={2017}}
@inproceedings{dhiman2017spectro,author={Dhiman, J. K. and Adiga, N. and Seelamantula, C. S.},booktitle={INTERSPEECH},pages={2306--2310},title={A Spectro-Temporal Demodulation Technique for Pitch Estimation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:yB1At4FlUx8C},year={2017}}
@inproceedings{kumar2017automatic,author={Kumar, J. R. H. and Harsha, S. and Kamath, Y. and Jampala, R. and Seelamantula, C. S.},booktitle={IEEE Region 10 Conference (TENCON)},pages={25--30},title={Automatic optic cup segmentation using K{\aa}sa's circle fitting technique},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:epqYDVWIO7EC},year={2017}}
@inproceedings{mukherjee2017dnns,author={Mukherjee, S. and Mahapatra, D. and Seelamantula, C. S.},booktitle={NIPS Bayesian Deep Learning Workshop},title={DNNs for sparse coding and dictionary learning},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:uc_IGeMz5qoC},year={2017}}
@inproceedings{vijayan2017time,author={Vijayan, K. and Dhiman, J. K. and Seelamantula, C. S.},booktitle={Interspeech},pages={329--333},title={Time-Frequency Coherence for Periodic-Aperiodic Decomposition of Speech Signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:nrtMV_XWKgEC},year={2017}}
@inproceedings{kumar2017automatic_macular,author={Kumar, J. R. H. and Adhikari, R. and Kamath, Y. and Jampala, R. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={1362--1366},title={Automatic delineation of macular regions based on a locally defined contrast function},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:AvfA0Oy_GE0C},year={2017}}
@inproceedings{bansal2017adaptive,author={Bansal, S. and Ghosh, A. and Seelamantula, C. S. and Gurrala, G. and Ghosh, P. K.},booktitle={IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)},pages={1--6},title={Adaptive frequency estimation using iterative DESA with RDFT-based filter},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:URolC5Kub84C},year={2017}}
@inproceedings{bhowmik2017bayesian,author={Bhowmik, A. and Adiga, A. and Seelamantula, C. and Hauser, F. and Jacak, J. and Heise, B.},booktitle={Proc. 2nd Neural Inf. Process. Syst. Workshop Bayesian Deep Learn},title={Bayesian deep deconvolutional neural networks},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ipzZ9siozwsC},year={2017}}
@article{mukherjee2017online,author={Mukherjee, S. and Chen, H. and Veeraraghavan, A. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1706.09585},title={Online Reweighted Least Squares Algorithm for Sparse Recovery and Application to Short-Wave Infrared Imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:j8SEvjWlNXcC},year={2017}}
@article{sadasivan2017prose,author={Sadasivan, J. and Seelamantula, C. S. and Muraka, N. R.},journal={arXiv preprint arXiv:1710.03975},title={PROSE: Perceptual Risk Optimization for Speech Enhancement},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:tKAzc9rXhukC},year={2017}}
@article{adiga2017non,author={Adiga, A. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1708.07370},title={A Non-Convex Optimization Technique for Sparse Blind Deconvolution--Initialization Aspects and Error Reduction Properties},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:b1wdh0AR-JQC},year={2017}}
@article{adiga2017alternating,author={Adiga, A. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1708.07370},title={AN ALTERNATING MINIMIZATION STRATEGY FOR SPARSE BLIND DECONVOLUTION- CONVERGENCE ANALYSIS AND CONCENTRATION INEQUALITIES},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:XD-gHx7UXLsC},year={2017}}
@article{mukherjee2016l1,author={Mukherjee, S. and Basu, R. and Seelamantula, C. S.},journal={Signal Processing},pages={42--52},title={ℓ₁-K-SVD: A robust dictionary learning algorithm with simultaneous update},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:D_sINldO8mEC},volume={123},year={2016}}
@article{sadasivan2016digitization,author={Sadasivan, H. and Patni, A. and Mulleti, S. and Seelamantula, C. S.},journal={Innovative Computer Sciences Journal},number={1},pages={1--10},title={Digitization of Electrocardiogram Using Bilateral Filtering},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:u-coK7KVo8oC},volume={2},year={2016}}
ICASSP 16
Joint dictionary training for bandwidth extension of speech signals
@inproceedings{sadasivan2016joint,author={Sadasivan, J. and Mukherjee, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Joint dictionary training for bandwidth extension of speech signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:bnK-pcrLprsC},year={2016}}
ICIP 16
Automatic segmentation of common carotid artery in transverse mode ultrasound images
@inproceedings{kumar2016automatic,author={Kumar, J. R. H. and Seelamantula, C. S. and Narayan, N. S. and Marziliano, P.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={389--393},title={Automatic segmentation of common carotid artery in transverse mode ultrasound images},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_Ybze24A_UAC},year={2016}}
ICIP 16
Active-disc-based Kalman filter technique for tracking of blood cells in microfluidic channels
@inproceedings{arvind2016active,author={Arvind, B. C. and Nagaraj, S. K. and Seelamantula, C. S. and Gorthi, S. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={3394--3398},title={Active-disc-based Kalman filter technique for tracking of blood cells in microfluidic channels},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:5awf1xo2G04C},year={2016}}
IEEE TSP 16
FRI sampling on structured nonuniform grids—Application to super-resolved optical imaging
@article{mulleti2016fri,author={Mulleti, S. and Shenoy, B. A. and Seelamantula, C. S.},journal={IEEE Transactions on Signal Processing},number={15},pages={3841--3853},title={FRI sampling on structured nonuniform grids—Application to super-resolved optical imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:5ugPr518TE4C},volume={64},year={2016}}
@article{upadhya2016risk,author={Upadhya, K. and Seelamantula, C. S. and Hari, K. V. S.},journal={Signal Processing},pages={78--87},title={A risk minimization framework for channel estimation in OFDM systems},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:cFHS6HbyZ2cC},volume={128},year={2016}}
@inproceedings{mukherjee2016convergence,author={Mukherjee, S. and Seelamantula, C. S.},booktitle={Twenty second national conference on communication (NCC)},pages={1--6},title={Convergence rate analysis of smoothed LASSO},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:WqliGbK-hY8C},year={2016}}
@inproceedings{seelamantula2016phase,author={Seelamantula, C. S.},booktitle={INTERSPEECH},pages={1775--1779},title={Phase-Encoded Speech Spectrograms},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:N5tVd3kTz84C},year={2016}}
Sādhanā 16
Optimum short-time polynomial regression for signal analysis
@article{murthy2016optimum,author={Murthy, A. S. and Seelamantula, C. S. and Sreenivas, T. V.},journal={Sādhanā},pages={1245--1260},title={Optimum short-time polynomial regression for signal analysis},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:PR6Y55bgFSsC},volume={41},year={2016}}
ICASSP 16
A divide-and-conquer dictionary learning algorithm and its performance analysis
@inproceedings{mukherjee2016divide,author={Mukherjee, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={A divide-and-conquer dictionary learning algorithm and its performance analysis},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:XiVPGOgt02cC},year={2016}}
NCC 16
An efficient formulation and parameter selection for multiple image super-resolution
@inproceedings{sekuboyina2016efficient,author={Sekuboyina, A. K. and Seelamantula, C. S.},booktitle={Twenty Second National Conference on Communication (NCC)},pages={1--6},title={An efficient formulation and parameter selection for multiple image super-resolution},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:VL0QpB8kHFEC},year={2016}}
ICVGIP 16
A distribution-independent risk estimator for image denoising
@inproceedings{subramanian2016distribution,author={Subramanian, B. K. and Gupta, A. and Seelamantula, C. S.},booktitle={Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP)},title={A distribution-independent risk estimator for image denoising},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:dQ2og3OwTAUC},year={2016}}
ICASSP 16
An unbiased risk estimator for Gaussian mixture noise distributions—Application to speech denoising
@inproceedings{sadasivan2016unbiased,author={Sadasivan, J. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={An unbiased risk estimator for Gaussian mixture noise distributions—Application to speech denoising},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:olpn-zPbct0C},year={2016}}
NCC 16
Sampling and reconstruction of time-limited signals using sum-of-sincs kernel
@inproceedings{mulleti2016sampling,author={Mulleti, S. and Seelamantula, C. S.},booktitle={Twenty Second National Conference on Communication (NCC)},pages={1--6},title={Sampling and reconstruction of time-limited signals using sum-of-sincs kernel},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ye4kPcJQO24C},year={2016}}
NCC 16
Efficient resampling of speech/audio signals in shift-invariant spaces
@inproceedings{sreedevi2016efficient,author={Sreedevi, G. and Narayanamurthy, P. K. and Seelamantula, C. S.},booktitle={Twenty Second National Conference on Communication (NCC)},pages={1--5},title={Efficient resampling of speech/audio signals in shift-invariant spaces},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:LjlpjdlvIbIC},year={2016}}
INTERSPEECH 16
A Novel Risk-Estimation-Theoretic Framework for Speech Enhancement in Nonstationary and Non-Gaussian Noise Conditions
@inproceedings{sadasivan2016novel,author={Sadasivan, J. and Seelamantula, C. S.},booktitle={INTERSPEECH},pages={3718--3722},title={A Novel Risk-Estimation-Theoretic Framework for Speech Enhancement in Nonstationary and Non-Gaussian Noise Conditions},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ZuybSZzF8UAC},year={2016}}
@inproceedings{gubbi2016how,author={Gubbi, S. V. and Gupta, A. and Seelamantula, C. S.},booktitle={Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP)},title={How much can a Gaussian smoother denoise?},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:hkOj_22Ku90C},year={2016}}
arXiv 16
Super-Resolution From Binary Measurements With Unknown Threshold
@article{mukherjee2016super,author={Mukherjee, S. and Sekuboyina, A. K. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1606.03472},title={Super-Resolution From Binary Measurements With Unknown Threshold},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:t6usbXjVLHcC},year={2016}}
INTERSPEECH 16
Reverberation-Robust One-Bit TDOA Based Moving Source Localization for Automatic Camera Steering
@inproceedings{sundar2016reverberation,author={Sundar, H. and Deepak, M. G. and Sreenivas, T. V. and Seelamantula, C. S.},booktitle={Proc. Interspeech},pages={3364--3368},title={Reverberation-Robust One-Bit TDOA Based Moving Source Localization for Automatic Camera Steering},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:1yQoGdGgb4wC},volume={2016},year={2016}}
2015
IEEE TIP 15
Ellipse fitting using the finite rate of innovation sampling principle
@article{mulleti2015ellipse,author={Mulleti, S. and Seelamantula, C. S.},journal={IEEE Transactions on Image Processing},number={3},pages={1451--1464},title={Ellipse fitting using the finite rate of innovation sampling principle},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:eflP2zaiRacC},volume={25},year={2015}}
GlobalSIP 15
Active discs for automated optic disc segmentation
@inproceedings{kumar2015active,author={Kumar, J. R. H. and Pediredla, A. K. and Seelamantula, C. S.},booktitle={IEEE global conference on signal and information processing (GlobalSIP)},title={Active discs for automated optic disc segmentation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:V3AGJWp-ZtQC},year={2015}}
IEEE TSP 15
Exact phase retrieval in principal shift-invariant spaces
@article{shenoy2015exact,author={Shenoy, B. A. and Mulleti, S. and Seelamantula, C. S.},journal={IEEE Transactions on Signal Processing},number={2},pages={406--416},title={Exact phase retrieval in principal shift-invariant spaces},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:VOx2b1Wkg3QC},volume={64},year={2015}}
IEEE TSP 15
Spectral zero-crossings: Localization properties and applications
@article{shenoy2015spectral,author={Shenoy, R. R. and Seelamantula, C. S.},journal={IEEE transactions on signal processing},number={12},pages={3177--3190},title={Spectral zero-crossings: Localization properties and applications},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:tOudhMTPpwUC},volume={63},year={2015}}
IEEE/ACM TASLP 15
Demodulation of narrowband speech spectrograms using the Riesz transform
@article{aragonda2015demodulation,author={Aragonda, H. and Seelamantula, C. S.},journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},number={11},pages={2017--2028},title={Demodulation of narrowband speech spectrograms using the Riesz transform},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:B3FOqHPlNUQC},volume={23},year={2015}}
@inproceedings{seelamantula2015image,author={Seelamantula, C. S. and Blu, T.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={1528--1532},title={Image denoising in multiplicative noise},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:sSrBHYA8nusC},year={2015}}
ICASSP 15
FRI sampling and reconstruction of asymmetric pulses
@inproceedings{nagesh2015fri,author={Nagesh, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={FRI sampling and reconstruction of asymmetric pulses},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:geHnlv5EZngC},year={2015}}
J. Electron. Imaging 15
Patch-based and multiresolution optimum bilateral filters for denoising images corrupted by Gaussian noise
@article{kishan2015patch,author={Kishan, H. and Seelamantula, C. S.},journal={Journal of Electronic imaging},number={5},pages={053021},title={Patch-based and multiresolution optimum bilateral filters for denoising images corrupted by Gaussian noise},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:fQNAKQ3IYiAC},volume={24},year={2015}}
IEEE SPL 15
A zero-crossing rate property of power complementary analysis filterbank outputs
@article{shenoy2015zero,author={Shenoy, R. R. and Seelamantula, C. S.},journal={IEEE signal processing letters},number={12},pages={2354--2358},title={A zero-crossing rate property of power complementary analysis filterbank outputs},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:LPZeul_q3PIC},volume={22},year={2015}}
@inproceedings{venkatesh2015directional,author={Venkatesh, M. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Directional bilateral filters},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:yD5IFk8b50cC},year={2015}}
AIP Adv. 15
Directional bilateral filters for smoothing fluorescence microscopy images
@article{venkatesh2015directional_smoothing,author={Venkatesh, M. and Mohan, K. and Seelamantula, C. S.},journal={AIP Advances},number={8},title={Directional bilateral filters for smoothing fluorescence microscopy images},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:5Ul4iDaHHb8C},volume={5},year={2015}}
@inproceedings{mulleti2015periodic,author={Mulleti, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Periodic non-uniform sampling for FRI signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:8AbLer7MMksC},year={2015}}
15
Opera: Operator-based annihilation for finite-rate-of-innovation signal sampling
@incollection{seelamantula2015opera,author={Seelamantula, C. S.},booktitle={Sampling Theory, a Renaissance: Compressive Sensing and Other Developments},publisher={Springer},title={Opera: Operator-based annihilation for finite-rate-of-innovation signal sampling},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:eJXPG6dFmWUC},year={2015}}
SampTA 15
An FRI model for asymmetric pulse trains and characterization of ventricular hypertrophy condition
@inproceedings{nagesh2015fri_model,author={Nagesh, S. and Seelamantula, C. S.},booktitle={Proc. Int. Conf. Sampling Theory Appl., Special Session Sampling Signals},title={An FRI model for asymmetric pulse trains and characterization of ventricular hypertrophy condition},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:08ZZubdj9fEC},year={2015}}
TENCON 15
Dictionary-learning-based post-filter for HMM-based speech synthesis
@inproceedings{narayanamurthy2015dictionary,author={Narayanamurthy, P. K. and Seelamantula, C. S.},booktitle={IEEE Region 10 Conference (TENCON)},pages={1--5},title={Dictionary-learning-based post-filter for HMM-based speech synthesis},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:BrmTIyaxlBUC},year={2015}}
J. Thromb. Haemost. 15
Nuances of NOAC’S in a developing country-a single center experience
S.
Iyengar, R.
Ravibabu, S.
Lakshman
, A.
Shenoy, G.
Sridhar, M.
Fulmali, and
others
@article{iyengar2015nuances,author={Iyengar, S. and Ravibabu, R. and Lakshman, S. and Shenoy, A. and Sridhar, G. and Fulmali, M. and others},journal={JOURNAL OF THROMBOSIS AND HAEMOSTASIS},pages={639--639},title={Nuances of NOAC'S in a developing country-a single center experience},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:kRWSkSYxWN8C},volume={13},year={2015}}
2014
IEEE TSP 14
Fienup algorithm with sparsity constraints: Application to frequency-domain optical-coherence tomography
@article{mukherjee2014fienup,author={Mukherjee, S. and Seelamantula, C. S.},journal={IEEE Transactions on Signal Processing},number={18},pages={4659--4672},title={Fienup algorithm with sparsity constraints: Application to frequency-domain optical-coherence tomography},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:g5m5HwL7SMYC},volume={62},year={2014}}
IEEE TSP 14
Super-resolution reconstruction in frequency-domain optical-coherence tomography using the finite-rate-of-innovation principle
@article{seelamantula2014super,author={Seelamantula, C. S. and Mulleti, S.},journal={IEEE Transactions on Signal Processing},number={19},pages={5020--5029},title={Super-resolution reconstruction in frequency-domain optical-coherence tomography using the finite-rate-of-innovation principle},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:3s1wT3WcHBgC},volume={62},year={2014}}
@article{venkitaraman2014auditory,author={Venkitaraman, A. and Adiga, A. and Seelamantula, C. S.},journal={Signal Processing},pages={608--619},title={Auditory-motivated Gammatone wavelet transform},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:JV2RwH3_ST0C},volume={94},year={2014}}
Signal Process. 14
Fractional Hilbert transform extensions and associated analytic signal construction
@article{venkitaraman2014fractional,author={Venkitaraman, A. and Seelamantula, C. S.},journal={Signal processing},pages={359--372},title={Fractional Hilbert transform extensions and associated analytic signal construction},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:R3hNpaxXUhUC},volume={94},year={2014}}
ICIP 14
Ultrasound image reconstruction using the finite-rate-of-innovation principle
@inproceedings{mulleti2014ultrasound,author={Mulleti, S. and Nagesh, S. and Langoju, R. and Patil, A. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={1728--1732},title={Ultrasound image reconstruction using the finite-rate-of-innovation principle},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:f2IySw72cVMC},year={2014}}
ICASSP 14
An optimum shrinkage estimator based on minimum-probability-of-error criterion and application to signal denoising
@inproceedings{sadasivan2014optimum,author={Sadasivan, J. and Mukherjee, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={An optimum shrinkage estimator based on minimum-probability-of-error criterion and application to signal denoising},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:pqnbT2bcN3wC},year={2014}}
@inproceedings{menon2014robust,author={Menon, S. V. and Seelamantula, C. S.},booktitle={19th International Conference on Digital Signal Processing (DSP)},pages={688--693},title={Robust savitzky-golay filters},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:4OULZ7Gr8RgC},year={2014}}
IEEE TSP 14
Exact phase retrieval for a class of 2-D parametric signals
@article{shenoy2014exact_parametric,author={Shenoy, B. A. and Seelamantula, C. S.},journal={IEEE Transactions on Signal Processing},number={1},pages={90--103},title={Exact phase retrieval for a class of 2-D parametric signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:a0OBvERweLwC},volume={63},year={2014}}
NCC 14
Zero-crossings based spectrum sensing under noise uncertainties
@inproceedings{gurugopinath2014zero,author={Gurugopinath, S. and Murthy, C. R. and Seelamantula, C. S.},booktitle={Twentieth National Conference on Communications (NCC)},pages={1--6},title={Zero-crossings based spectrum sensing under noise uncertainties},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:35N4QoGY0k4C},year={2014}}
PLoS ONE 14
Quantifying vocal mimicry in the greater racket-tailed drongo: a comparison of automated methods and human assessment
@article{agnihotri2014quantifying,author={Agnihotri, S. and Sundeep, P. and Seelamantula, C. S. and Balakrishnan, R.},journal={PloS one},number={3},pages={e89540},title={Quantifying vocal mimicry in the greater racket-tailed drongo: a comparison of automated methods and human assessment},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:NaGl4SEjCO4C},volume={9},year={2014}}
IEEE/ACM TASLP 14
Binaural signal processing motivated generalized analytic signal construction and AM-FM demodulation
@article{venkitaraman2014binaural,author={Venkitaraman, A. and Seelamantula, C. S.},journal={IEEE/ACM transactions on audio, speech, and language processing},number={6},pages={1023--1033},title={Binaural signal processing motivated generalized analytic signal construction and AM-FM demodulation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:lSLTfruPkqcC},volume={22},year={2014}}
DSP 14
An alternating \(\ell_p\)-\(\ell_2\)projections algorithm (ALPA) for speech modeling using sparsity constraints
@inproceedings{adiga2014alternating,author={Adiga, A. and Seelamantula, C. S.},booktitle={19th International Conference on Digital Signal Processing (DSP)},pages={291--296},title={An alternating {\(\ell_p\)}-{\(\ell_2\)}projections algorithm (ALPA) for speech modeling using sparsity constraints},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:rO6llkc54NcC},year={2014}}
ICASSP 14
On the role of the Hilbert transform in boosting the performance of the annihilating filter
@inproceedings{nagesh2014role,author={Nagesh, S. and Mulleti, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={On the role of the Hilbert transform in boosting the performance of the annihilating filter},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:SeFeTyx0c_EC},year={2014}}
ICASSP 14
Frequency domain linear prediction based on temporal analysis
@inproceedings{shenoy2014frequency,author={Shenoy, R. R. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Frequency domain linear prediction based on temporal analysis},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:HoB7MX3m0LUC},year={2014}}
arXiv 14
A split-and-merge dictionary learning algorithm for sparse representation
@article{mukherjee2014split,author={Mukherjee, S. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1403.4781},title={A split-and-merge dictionary learning algorithm for sparse representation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:J_g5lzvAfSwC},year={2014}}
DSP 14
An unbiased risk estimator for multiplicative noise—Application to 1-D signal denoising
@inproceedings{panisetti2014unbiased,author={Panisetti, B. K. and Blu, T. and Seelamantula, C. S.},booktitle={19th International Conference on Digital Signal Processing (DSP)},pages={497--502},title={An unbiased risk estimator for multiplicative noise—Application to 1-D signal denoising},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:zA6iFVUQeVQC},year={2014}}
IEEE SPL 14
Spatially adaptive kernel regression using risk estimation
@article{krishnan2014spatially,author={Krishnan, S. R. and Seelamantula, C. S. and Chakravarti, P.},journal={IEEE Signal Processing Letters},number={4},pages={445--448},title={Spatially adaptive kernel regression using risk estimation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:GnPB-g6toBAC},volume={21},year={2014}}
arXiv 14
Risk Estimation Without Using Stein’s Lemma–Application to Image Denoising
@article{gubbi2014risk,author={Gubbi, S. V. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1412.2210},title={Risk Estimation Without Using Stein's Lemma--Application to Image Denoising},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:D03iK_w7-QYC},year={2014}}
arXiv 14
A risk minimization framework for channel estimation in OFDM systems
@article{upadhya2014risk,author={Upadhya, K. and Seelamantula, C. S. and Hari, K. V. S.},journal={arXiv preprint arXiv:1410.6028},title={A risk minimization framework for channel estimation in OFDM systems},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:OTTXONDVkokC},year={2014}}
ICASSP 14
Ellipse fitting using finite rate of innovation principles
@inproceedings{mulleti2014ellipse,author={Mulleti, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Ellipse fitting using finite rate of innovation principles},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ZHo1McVdvXMC},year={2014}}
ICIP 14
Controlled blurring for improving image reconstruction quality in flutter-shutter acquisition
@inproceedings{srinivas2014controlled,author={Srinivas, S. and Adiga, A. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={5826--5830},title={Controlled blurring for improving image reconstruction quality in flutter-shutter acquisition},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_xSYboBqXhAC},year={2014}}
ICIP 14
Exact reconstruction in quantitative phase microscopy
@inproceedings{seelamantula2014exact,author={Seelamantula, C. S. and Shenoy, B. A. and Coquoz, S. and Lasser, T.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={3934--3938},title={Exact reconstruction in quantitative phase microscopy},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:bFI3QPDXJZMC},year={2014}}
DSP 14
Optimum parameter selection in sparse reconstruction of frequency-domain optical-coherence tomography signals
@inproceedings{krishnan2014optimum,author={Krishnan, S. R. and Seelamantula, C. S.},booktitle={19th International Conference on Digital Signal Processing (DSP)},pages={200--203},title={Optimum parameter selection in sparse reconstruction of frequency-domain optical-coherence tomography signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:fPk4N6BV_jEC},year={2014}}
IEEE SPL 14
A contraction mapping approach for robust estimation of lagged autocorrelation
@article{seelamantula2014contraction,author={Seelamantula, C. S. and Shenoy, R. R.},journal={IEEE Signal Processing Letters},number={9},pages={1054--1058},title={A contraction mapping approach for robust estimation of lagged autocorrelation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:2P1L_qKh6hAC},volume={21},year={2014}}
CoRR 14
A Robust Dictionary Learning Algorithm for Image Denoising
@article{mukherjee2014robust,author={Mukherjee, S. and Basu, R. and Seelamantula, C. S.},journal={CoRR},title={A Robust Dictionary Learning Algorithm for Image Denoising},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:dfsIfKJdRG4C},volume={abs/1403.4781},year={2014}}
DSP 14
A split-and-merge dictionary learning algorithm for sparse representation: Application to image denoising
@inproceedings{mukherjee2014split_merge,author={Mukherjee, S. and Seelamantula, C. S.},booktitle={19th International Conference on Digital Signal Processing (DSP)},pages={310--315},title={A split-and-merge dictionary learning algorithm for sparse representation: Application to image denoising},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:u_35RYKgDlwC},year={2014}}
2013
TENCON 13
Gammatone wavelet cepstral coefficients for robust speech recognition
@inproceedings{adiga2013gammatone,author={Adiga, A. and Magimai, M. and Seelamantula, C. S.},booktitle={IEEE international conference of IEEE region 10 (TENCON)},pages={1--4},title={Gammatone wavelet cepstral coefficients for robust speech recognition},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:hMod-77fHWUC},year={2013}}
@inproceedings{jose2013bilateral,author={Jose, A. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Bilateral edge detectors},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:4JMBOYKVnBMC},year={2013}}
IEEE SPL 13
A Savitzky-Golay filtering perspective of dynamic feature computation
@article{krishnan2013savitzky,author={Krishnan, S. R. and Doss, M. M. and Seelamantula, C. S.},journal={IEEE Signal Processing Letters},number={3},pages={281--284},title={A Savitzky-Golay filtering perspective of dynamic feature computation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:hC7cP41nSMkC},volume={20},year={2013}}
ICIP 13
A shape-template based two-stage corpus callosum segmentation technique for sagittal plane T1-weighted brain magnetic resonance images
@inproceedings{mogali2013shape,author={Mogali, J. K. and Nallapareddy, N. and Seelamantula, C. S. and Unser, M.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={1177--1181},title={A shape-template based two-stage corpus callosum segmentation technique for sagittal plane T1-weighted brain magnetic resonance images},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:r0BpntZqJG4C},year={2013}}
@article{venkitaraman2013temporal,author={Venkitaraman, A. and Seelamantula, C. S.},journal={IEEE Signal Processing Letters},number={12},pages={1191--1194},title={Temporal envelope fit of transient audio signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:maZDTaKrznsC},volume={20},year={2013}}
ICIP 13
Ridge detection using savitzky-golay filtering and steerable second-order gaussian derivatives
@inproceedings{jose2013ridge,author={Jose, A. and Krishnan, S. R. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={3059--3063},title={Ridge detection using savitzky-golay filtering and steerable second-order gaussian derivatives},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:isC4tDSrTZIC},year={2013}}
ICIP 13
SURE-optimal two-dimensional Savitzky-Golay filters for image denoising
@inproceedings{menon2013sure,author={Menon, S. V. and Seelamantula, C. S.},booktitle={IEEE International Conference on Image Processing (ICIP)},pages={459--463},title={SURE-optimal two-dimensional Savitzky-Golay filters for image denoising},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:bEWYMUwI8FkC},year={2013}}
@article{mogali2013template,author={Mogali, J. K. and Pediredla, A. K. and Seelamantula, C. S.},journal={arXiv preprint arXiv:1312.0760},title={Template-based active contours},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:blknAaTinKkC},year={2013}}
TENCON 13
Cell tracking using particle filters and level sets
@inproceedings{vishwanath2013cell,author={Vishwanath, B. and Seelamantula, C. S.},booktitle={IEEE International Conference of IEEE Region 10 (TENCON)},pages={1--4},title={Cell tracking using particle filters and level sets},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:YFjsv_pBGBYC},year={2013}}
CONECCT 13
A multichannel sampling method for 2-D finite-rate-of-innovation signals
@inproceedings{mulleti2013multichannel,author={Mulleti, S. and Shenoy, B. A. and Seelamantula, C. S.},booktitle={IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)},title={A multichannel sampling method for 2-D finite-rate-of-innovation signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:mB3voiENLucC},year={2013}}
IEEE SPL 13
On computing amplitude, phase, and frequency modulations using a vector interpretation of the analytic signal
@article{venkitaraman2013computing,author={Venkitaraman, A. and Seelamantula, C. S.},journal={IEEE Signal Processing Letters},number={12},pages={1187--1190},title={On computing amplitude, phase, and frequency modulations using a vector interpretation of the analytic signal},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:k_IJM867U9cC},volume={20},year={2013}}
J. Indian Inst. Sci. 13
Hilbert transform relations in frequency-domain optical-coherence tomographic imaging
@article{seelamantula2013hilbert,author={Seelamantula, C. S. and Lasser, T.},journal={Journal of the Indian Institute of Science},number={1},pages={139--148},title={Hilbert transform relations in frequency-domain optical-coherence tomographic imaging},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:HDshCWvjkbEC},volume={93},year={2013}}
SPARS 13
Sparse signal reconstruction in shift-invariant spaces
M.
Satish
, A.
Shenoy, and S.
Chandra
In Signal Processing with Adaptive Sparse Structured Representations, Apr 2013
@inproceedings{satish2013sparse,author={Satish, M. and Shenoy, A. and Chandra, S.},booktitle={Signal Processing with Adaptive Sparse Structured Representations},pages={665--668},title={Sparse signal reconstruction in shift-invariant spaces},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_Qo2XoVZTnwC},year={2013}}
ICASSP 13
Riesz-transform-based demodulation of narrowband spectrograms of voiced speech
@inproceedings{aragonda2013riesz,author={Aragonda, H. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Riesz-transform-based demodulation of narrowband spectrograms of voiced speech},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:RHpTSmoSYBkC},year={2013}}
JASA 13
Spectral-envelope–group-delay models for transients
@article{shenoy2013spectral_envelope,author={Shenoy, R. R. and Seelamantula, C. S.},journal={The Journal of the Acoustical Society of America},number={5},pages={2788--2802},title={Spectral-envelope–group-delay models for transients},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:TQgYirikUcIC},volume={133},year={2013}}
2012
IEEE TSP 12
On the selection of optimum Savitzky-Golay filters
@article{krishnan2012selection,author={Krishnan, S. R. and Seelamantula, C. S.},journal={IEEE transactions on signal processing},number={2},pages={380--391},title={On the selection of optimum Savitzky-Golay filters},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:dhFuZR0502QC},volume={61},year={2012}}
ICASSP 12
An iterative algorithm for phase retrieval with sparsity constraints: application to frequency domain optical coherence tomography
@inproceedings{mukherjee2012iterative,author={Mukherjee, S. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={An iterative algorithm for phase retrieval with sparsity constraints: application to frequency domain optical coherence tomography},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:4TOpqqG69KYC},year={2012}}
@inproceedings{kishan2012sure,author={Kishan, H. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={SURE-fast bilateral filters},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_kc_bZDykSQC},year={2012}}
ICASSP 12
A unified approach for optimization of snakuscules and ovuscules
@inproceedings{pediredla2012unified,author={Pediredla, A. K. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={A unified approach for optimization of snakuscules and ovuscules},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Wp0gIr-vW9MC},year={2012}}
IEEE TASLP 12
A mixture model approach for formant tracking and the robustness of student’s-t distribution
@article{sundar2012mixture,author={Sundar, H. and Seelamantula, C. S. and Sreenivas, T. V.},journal={IEEE transactions on audio, speech, and language processing},number={10},pages={2626--2636},title={A mixture model approach for formant tracking and the robustness of student's-t distribution},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:YOwf2qJgpHMC},volume={20},year={2012}}
IEEE SPL 12
A technique to compute smooth amplitude, phase, and frequency modulations from the analytic signal
@article{venkitaraman2012technique,author={Venkitaraman, A. and Seelamantula, C. S.},journal={IEEE Signal Processing Letters},number={10},pages={623--626},title={A technique to compute smooth amplitude, phase, and frequency modulations from the analytic signal},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ULOm3_A8WrAC},volume={19},year={2012}}
SPCOM 12
Spectral zero-crossings: Localization properties and application to epoch extraction in speech signals
@inproceedings{shenoy2012spectral,author={Shenoy, R. R. and Seelamantula, C. S.},booktitle={International Conference on Signal Processing and Communications (SPCOM)},title={Spectral zero-crossings: Localization properties and application to epoch extraction in speech signals},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:qxL8FJ1GzNcC},year={2012}}
STSIP 12
A non-iterative phase retrieval algorithm for minimum-phase signals using the annihilating filter
@article{mukherjee2012non,author={Mukherjee, S. and Seelamantula, C. S.},journal={Sampling Theory in Signal and Image Processing},pages={165--193},title={A non-iterative phase retrieval algorithm for minimum-phase signals using the annihilating filter},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:70eg2SAEIzsC},volume={11},year={2012}}
Opt. Lett. 12
Optimal sparsifying bases for frequency-domain optical-coherence tomography
@article{nayak2012optimal,author={Nayak, R. and Seelamantula, C. S.},journal={Optics Letters},number={23},pages={4907--4909},title={Optimal sparsifying bases for frequency-domain optical-coherence tomography},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:7PzlFSSx8tAC},volume={37},year={2012}}
ICIP 12
Optimal parameter selection for bilateral filters using Poisson Unbiased Risk Estimate
@inproceedings{kishan2012optimal,author={Kishan, H. and Seelamantula, C. S.},booktitle={19th IEEE International Conference on Image Processing (ICIP)},pages={121--124},title={Optimal parameter selection for bilateral filters using Poisson Unbiased Risk Estimate},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:-f6ydRqryjwC},year={2012}}
ICIP 12
Bilateral smoothing of gradient vector field and application to image segmentation
@inproceedings{hegadi2012bilateral,author={Hegadi, R. S. and Pediredla, A. K. and Seelamantula, C. S.},booktitle={19th IEEE International Conference on Image Processing (ICIP)},pages={317--320},title={Bilateral smoothing of gradient vector field and application to image segmentation},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:hFOr9nPyWt4C},year={2012}}
SPCOM 12
Some new results on signal reconstruction from Fourier transform magnitude spectrum
@inproceedings{seelamantula2012some,author={Seelamantula, C. S.},booktitle={International Conference on Signal Processing and Communications (SPCOM)},title={Some new results on signal reconstruction from Fourier transform magnitude spectrum},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:aqlVkmm33-oC},year={2012}}
SAPA-INTERSPEECH 12
A generalized Stein’s estimation approach for speech enhancement based on perceptual criteria
@inproceedings{krishnan2012generalized,author={Krishnan, S. R. and Seelamantula, C. S.},booktitle={SAPA@ INTERSPEECH},pages={28--33},title={A generalized Stein's estimation approach for speech enhancement based on perceptual criteria},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:mVmsd5A6BfQC},year={2012}}
SPCOM 12
A risk-estimation-based formulation for speech enhancement and its relation to wiener filtering
@inproceedings{muraka2012risk,author={Muraka, N. R. and Seelamantula, C. S.},booktitle={International Conference on Signal Processing and Communications (SPCOM)},title={A risk-estimation-based formulation for speech enhancement and its relation to wiener filtering},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:4DMP91E08xMC},year={2012}}
JOSA A 12
Zero-crossing approach to high-resolution reconstruction in frequency-domain optical-coherence tomography
S. R.
Krishnan, C. S.
Seelamantula, A.
Bouwens, M.
Leutenegger, and T.
Lasser
Journal of the Optical Society of America A, Apr 2012
@article{krishnan2012zero,author={Krishnan, S. R. and Seelamantula, C. S. and Bouwens, A. and Leutenegger, M. and Lasser, T.},journal={Journal of the Optical Society of America A},number={10},pages={2080--2091},title={Zero-crossing approach to high-resolution reconstruction in frequency-domain optical-coherence tomography},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:M3ejUd6NZC8C},volume={29},year={2012}}
STSIP 12
SURE-optimal bandwidth selection in nonparametric regression
@article{krishnan2012sure,author={Krishnan, S. R. and Seelamantula, C. S.},journal={Sampling Theory in Signal and Image Processing},pages={133--163},title={SURE-optimal bandwidth selection in nonparametric regression},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:vV6vV6tmYwMC},volume={11},year={2012}}
AES 12
Spectral zero-crossings alone enable reliable estimation of interaural time difference
@inproceedings{shenoy2012spectral_zero,author={Shenoy, R. R. and Seelamantula, C. S.},booktitle={Audio Engineering Society Conference: 45th International Conference on Applications of Time-Frequency Processing in Audio},title={Spectral zero-crossings alone enable reliable estimation of interaural time difference},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:kNdYIx-mwKoC},year={2012}}
2011
INTERSPEECH 11
A Risk-Estimation-Based Comparison of Mean Square Error and Itakura-Saito Distortion Measures for Speech Enhancement
@inproceedings{muraka2011risk,author={Muraka, N. R. and Seelamantula, C. S.},booktitle={INTERSPEECH},pages={349--352},title={A Risk-Estimation-Based Comparison of Mean Square Error and Itakura-Saito Distortion Measures for Speech Enhancement},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:MXK_kJrjxJIC},year={2011}}
ISPA 11
Active-contour-based automated image quantitation techniques for western blot analysis
@inproceedings{pediredla2011active,author={Pediredla, A. K. and Seelamantula, C. S.},booktitle={7th International Symposium on Image and Signal Processing and Analysis (ISPA)},title={Active-contour-based automated image quantitation techniques for western blot analysis},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:5nxA0vEk-isC},year={2011}}
ISPA 11
A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images
@inproceedings{pediredla2011huber,author={Pediredla, A. K. and Seelamantula, C. S.},booktitle={7th International Symposium on Image and Signal Processing and Analysis (ISPA)},title={A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:hqOjcs7Dif8C},year={2011}}
NCC 11
Efficient post-processing techniques for speech enhancement
@inproceedings{ramakrishnan2011efficient,author={Ramakrishnan, V. and Shetty, K. and Pawan, K. G. and Seelamantula, C. S.},booktitle={National Conference on Communications (NCC)},pages={1--5},title={Efficient post-processing techniques for speech enhancement},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:WF5omc3nYNoC},year={2011}}
ICASSP 11
Quadrature approximation properties of the spiral-phase quadrature transform
@inproceedings{aragonda2011quadrature,author={Aragonda, H. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Quadrature approximation properties of the spiral-phase quadrature transform},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Se3iqnhoufwC},year={2011}}
ICASSP 11
Spectral-envelope and group-delay models for transient signals—Applications to castanets and stop consonants
@inproceedings{shenoy2011spectral_envelope,author={Shenoy, R. R. and Seelamantula, C. S.},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Spectral-envelope and group-delay models for transient signals—Applications to castanets and stop consonants},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:roLk4NBRz8UC},year={2011}}
SampTA 11
A finite-rate-of-innovation signal sampling perspective of the source-filter model of speech production
@inproceedings{seelamantula2011finite,author={Seelamantula, C. S.},booktitle={SampTA},title={A finite-rate-of-innovation signal sampling perspective of the source-filter model of speech production},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:3fE2CSJIrl8C},year={2011}}
2010
Phys. Med. Biol. 10
A fast time-domain algorithm for the assessment of tissue blood flow in laser-Doppler flowmetry
@article{binzoni2010fast,author={Binzoni, T. and Seelamantula, C. S. and Van De Ville, D.},journal={Physics in Medicine \& Biology},number={13},pages={N383},title={A fast time-domain algorithm for the assessment of tissue blood flow in laser-Doppler flowmetry},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Tyk-4Ss8FVUC},volume={55},year={2010}}
SPCOM 10
A sub-Nyquist sampling method for computing the level-crossing-times of an analog signal: Theory and applications
@inproceedings{seelamantula2010sub,author={Seelamantula, C. S.},booktitle={International Conference on Signal Processing and Communications (SPCOM)},title={A sub-Nyquist sampling method for computing the level-crossing-times of an analog signal: Theory and applications},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Y0pCki6q_DkC},year={2010}}
INTERSPEECH 10
A multimodal density function estimation approach to formant tracking
@inproceedings{harshavardhan2010multimodal,author={Harshavardhan, S. and Seelamantula, C. S. and Sreenivas, T. V.},booktitle={Proc. Interspeech},pages={2410--2413},title={A multimodal density function estimation approach to formant tracking},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:eQOLeE2rZwMC},volume={2010},year={2010}}
2009
JASA 09
Vocalizations of wild Asian elephants (Elephas maximus): structural classification and social context
@article{nair2009vocalizations,author={Nair, S. and Balakrishnan, R. and Seelamantula, C. S. and Sukumar, R.},journal={The Journal of the Acoustical Society of America},number={5},pages={2768--2778},title={Vocalizations of wild Asian elephants (Elephas maximus): structural classification and social context},url={https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:2osOgNQ5qMEC},volume={126},year={2009}}