An iterative algorithm for phase retrieval with sparsity constraints: application to frequency domain optical coherence tomography S Mukherjee, CS Seelamantula 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012 | 75 | 2012 |
Learned convex regularizers for inverse problems S Mukherjee, S Dittmer, Z Shumaylov, S Lunz, O Öktem, CB Schönlieb arxiv preprint (arXiv:2008.02839v1), 2020 | 56 | 2020 |
Fienup algorithm with sparsity constraints: Application to frequency-domain optical-coherence tomography S Mukherjee, CS Seelamantula Signal Processing, IEEE Transactions on 62 (18), 4659-4672, 2014 | 52 | 2014 |
Learned reconstruction methods with convergence guarantees: A survey of concepts and applications S Mukherjee, A Hauptmann, O Öktem, M Pereyra, CB Schönlieb IEEE Signal Processing Magazine 40 (1), 164-182, 2023 | 48 | 2023 |
ℓ1-K-SVD: A robust dictionary learning algorithm with simultaneous update S Mukherjee, R Basu, CS Seelamantula Signal Processing 123, 42-52, 2016 | 43 | 2016 |
End-to-end reconstruction meets data-driven regularization for inverse problems S Mukherjee, M Carioni, O Öktem, CB Schönlieb Thirty-Fifth Conference on Neural Information Processing Systems, 2021 | 36 | 2021 |
Learning convex regularizers satisfying the variational source condition for inverse problems S Mukherjee, CB Schönlieb, M Burger NeurIPS-2021 Workshop on Deep Learning and Inverse Problems, 2021 | 24 | 2021 |
An optimum shrinkage estimator based on minimum-probability-of-error criterion and application to signal denoising J Sadasivan, S Mukherjee, CS Seelamantula Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International …, 2014 | 24 | 2014 |
Joint dictionary training for bandwidth extension of speech signals J Sadasivan, S Mukherjee, CS Seelamantula 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 23 | 2016 |
Deep sparse coding using optimized linear expansion of thresholds D Mahapatra, S Mukherjee, CS Seelamantula arXiv preprint arXiv:1705.07290, 2017 | 18 | 2017 |
Data-driven mirror descent with input-convex neural networks HY Tan, S Mukherjee, J Tang, CB Schönlieb accepted to the SIAM Journal on Mathematics of Data Science (SIMODS), 2022 | 13 | 2022 |
Phase retrieval from binary measurements S Mukherjee, CS Seelamantula IEEE Signal Processing Letters 25 (3), 348-352, 2018 | 10 | 2018 |
A non-iterative phase retrieval algorithm for minimum-phase signals using the annihilating filter S Mukherjee, CS Seelamantula Sampling Theory in Signal and Image Processing 11, 165-193, 2012 | 8 | 2012 |
Tree species classification from hyperspectral data using graph-regularized neural networks D Bandyopadhyay, S Mukherjee, J Ball, G Vincent, DA Coomes, ... arXiv preprint arXiv:2208.08675, 2022 | 7 | 2022 |
Learned reconstruction methods with convergence guarantees S Mukherjee, A Hauptmann, O Öktem, M Pereyra, CB Schönlieb arXiv preprint arXiv:2206.05431, 2022 | 7 | 2022 |
Stochastic primal-dual deep unrolling J Tang, S Mukherjee, CB Schönlieb arXiv preprint arXiv:2110.10093, 2021 | 7 | 2021 |
Provably convergent plug-and-play quasi-newton methods HY Tan, S Mukherjee, J Tang, CB Schönlieb SIAM Journal on Imaging Sciences 17 (2), 785-819, 2024 | 6 | 2024 |
Convergent regularization in inverse problems and linear plug-and-play denoisers A Hauptmann, S Mukherjee, CB Schönlieb, F Sherry arXiv:2307.09441, 2023 | 6 | 2023 |
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers V Stergiopoulou, S Mukherjee, L Calatroni, L Blanc-Féraud International Conference on Scale Space and Variational Methods in Computer …, 2023 | 6 | 2023 |
Adversarially learned iterative reconstruction for imaging inverse problems S Mukherjee, O Öktem, CB Schönlieb International Conference on Scale Space and Variational Methods in Computer …, 2021 | 6 | 2021 |