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Subhadip Mukherjee
Subhadip Mukherjee
Assistant Professor, Department of E&ECE, IIT Kharagpur, India
Verified email at ece.iitkgp.ac.in - Homepage
Title
Cited by
Cited by
Year
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
742012
Learned convex regularizers for inverse problems
S Mukherjee, S Dittmer, Z Shumaylov, S Lunz, O Öktem, CB Schönlieb
arxiv preprint (arXiv:2008.02839v1), 2020
552020
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
522014
ℓ1-K-SVD: A robust dictionary learning algorithm with simultaneous update
S Mukherjee, R Basu, CS Seelamantula
Signal Processing 123, 42-52, 2016
422016
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
372023
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
352021
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
242014
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
232021
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
212016
Deep sparse coding using optimized linear expansion of thresholds
D Mahapatra, S Mukherjee, CS Seelamantula
arXiv preprint arXiv:1705.07290, 2017
172017
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
102022
Phase retrieval from binary measurements
S Mukherjee, CS Seelamantula
IEEE Signal Processing Letters 25 (3), 348-352, 2018
92018
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
82012
Learned reconstruction methods with convergence guarantees
S Mukherjee, A Hauptmann, O Öktem, M Pereyra, CB Schönlieb
arXiv preprint arXiv:2206.05431, 2022
72022
Stochastic primal-dual deep unrolling
J Tang, S Mukherjee, CB Schönlieb
arXiv preprint arXiv:2110.10093, 2021
72021
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
62023
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
62022
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
62021
O.\" Oktem, and C.-B. Sch\" onlieb, Learned Convex Regularizers for Inverse Problems
S Mukherjee, S Dittmer, Z Shumaylov, S Lunz
arXiv preprint arXiv:2008.02839, 2020
62020
DNNs for sparse coding and dictionary learning
S Mukherjee, D Mahapatra, CS Seelamantula
NIPS Bayesian Deep Learning Workshop, 2017
62017
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