| 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 | 96 | 2023 |
| Learned convex regularizers for inverse problems S Mukherjee, S Dittmer, Z Shumaylov, S Lunz, O Öktem, CB Schönlieb arxiv preprint (arXiv:2008.02839v1), 2020 | 88 | 2020 |
| 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 | 76 | 2012 |
| 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 | 58 | 2014 |
| 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 | 55 | 2021 |
| ℓ1-K-SVD: A robust dictionary learning algorithm with simultaneous update S Mukherjee, R Basu, CS Seelamantula Signal Processing 123, 42-52, 2016 | 47 | 2016 |
| 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 | 36 | 2024 |
| 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 | 34 | 2021 |
| Convergent regularization in inverse problems and linear plug-and-play denoisers A Hauptmann, S Mukherjee, CB Schönlieb, F Sherry arXiv:2307.09441, 2023 | 28 | 2023 |
| Deep learning-based Segmentation of Multi-site Disease in Ovarian Cancer T Buddenkotte, L Rundo, R Woitek, L Escudero Sanchez, L Beer, ... medRxiv, 2023 | 28 | 2023 |
| Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI S Saitta, M Carioni, S Mukherjee, CB Schönlieb, A Redaelli Computer methods and programs in biomedicine 246, 108057, 2024 | 27 | 2024 |
| Weakly convex regularisers for inverse problems: Convergence of critical points and primal-dual optimisation Z Shumaylov, J Budd, S Mukherjee, CB Schönlieb arXiv preprint arXiv:2402.01052, 2024 | 25 | 2024 |
| 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 | 25 | 2016 |
| 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 | 25 | 2014 |
| 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 | 20 | 2022 |
| Data-driven convex regularizers for inverse problems S Mukherjee, S Dittmer, Z Shumaylov, S Lunz, O Öktem, CB Schönlieb ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 17 | 2024 |
| Deep sparse coding using optimized linear expansion of thresholds D Mahapatra, S Mukherjee, CS Seelamantula arXiv preprint arXiv:1705.07290, 2017 | 17 | 2017 |
| Stochastic primal-dual deep unrolling J Tang, S Mukherjee, CB Schönlieb arXiv preprint arXiv:2110.10093, 2021 | 14 | 2021 |
| NF-ULA: Normalizing flow-based unadjusted Langevin algorithm for imaging inverse problems Z Cai, J Tang, S Mukherjee, J Li, CB Schönlieb, X Zhang SIAM Journal on Imaging Sciences 17 (2), 820-860, 2024 | 12 | 2024 |
| Provably convergent data-driven convex-nonconvex regularization Z Shumaylov, J Budd, S Mukherjee, CB Schönlieb arXiv preprint arXiv:2310.05812, 2023 | 12 | 2023 |
Carola-Bibiane SchönliebDAMTP, University of CambridgeVerified email at cam.ac.uk
