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Subhadip Mukherjee
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2020 – today
- 2024
- [j15]Simone Saitta
, Marcello Carioni, Subhadip Mukherjee
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI. Comput. Methods Programs Biomed. 246: 108057 (2024) - [j14]Subhadip Mukherjee, Somnath Mukhopadhyay
IoTSLE: Securing IoT systems in low-light environments through finite automata, deep learning and DNA computing based image steganographic model. Internet Things 28: 101358 (2024) - [j13]Sakhi Bandyopadhyay, Subhadip Mukherjee
Parallel BFS through pennant data structure with reducer hyper-object based data hiding for 3D mesh images. Secur. Priv. 7(5) (2024) - [j12]Hong Ye Tan, Subhadip Mukherjee
Provably Convergent Plug-and-Play Quasi-Newton Methods. SIAM J. Imaging Sci. 17(2): 785-819 (2024) - [j11]Ziruo Cai, Junqi Tang
NF-ULA: Normalizing Flow-Based Unadjusted Langevin Algorithm for Imaging Inverse Problems. SIAM J. Imaging Sci. 17(2): 820-860 (2024) - [j10]Stefano van Gogh
Data-Driven Gradient Regularization for Quasi-Newton Optimization in Iterative Grating Interferometry CT Reconstruction. IEEE Trans. Medical Imaging 43(3): 1033-1044 (2024) - [j9]Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb:
Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration. Trans. Mach. Learn. Res. 2024 (2024) - [c20]Ander Biguri, Subhadip Mukherjee:
Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam Computed Tomography (CT) Reconstruction. ICASSP Workshops 2024: 81-82 - [c19]Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov
Data-Driven Convex Regularizers for Inverse Problems. ICASSP 2024: 13386-13390 - [c18]Zakhar Shumaylov, Jeremy Budd
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation. ICML 2024 - [i27]Zakhar Shumaylov
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation. CoRR abs/2402.01052 (2024) - [i26]Hong Ye Tan, Ziruo Cai, Marcelo Pereyra, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb:
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation. CoRR abs/2404.05445 (2024) - [i25]Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb:
Blessing of Dimensionality for Approximating Sobolev Classes on Manifolds. CoRR abs/2408.06996 (2024) - [i24]Hok Shing Wong, Matthias J. Ehrhardt, Subhadip Mukherjee:
A Primal-dual algorithm for image reconstruction with ICNNs. CoRR abs/2410.12441 (2024) - 2023
- [j8]Hong Ye Tan
Data-Driven Mirror Descent with Input-Convex Neural Networks. SIAM J. Math. Data Sci. 5(2): 558-587 (2023) - [j7]Subhadip Mukherjee
Learned Reconstruction Methods With Convergence Guarantees: A survey of concepts and applications. IEEE Signal Process. Mag. 40(1): 164-182 (2023) - [c17]Hong Ye Tan
Robust Data-Driven Accelerated Mirror Descent. ICASSP 2023: 1-5 - [c16]Vasiliki Stergiopoulou, Subhadip Mukherjee, Luca Calatroni, Laure Blanc-Féraud:
Fluctuation-Based Deconvolution in Fluorescence Microscopy Using Plug-and-Play Denoisers. SSVM 2023: 498-510 - [i23]Simone Saitta, Marcello Carioni
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI. CoRR abs/2302.12835 (2023) - [i22]Hong Ye Tan
Provably Convergent Plug-and-Play Quasi-Newton Methods. CoRR abs/2303.07271 (2023) - [i21]Vasiliki Stergiopoulou, Subhadip Mukherjee, Luca Calatroni, Laure Blanc-Féraud:
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers. CoRR abs/2303.11212 (2023) - [i20]Ziruo Cai, Junqi Tang, Subhadip Mukherjee, Jinglai Li, Carola-Bibiane Schönlieb, Xiaoqun Zhang:
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems. CoRR abs/2304.08342 (2023) - [i19]Andreas Hauptmann, Subhadip Mukherjee, Carola-Bibiane Schönlieb, Ferdia Sherry:
Convergent regularization in inverse problems and linear plug-and-play denoisers. CoRR abs/2307.09441 (2023) - [i18]Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts
Dynamic Bilevel Learning with Inexact Line Search. CoRR abs/2308.10098 (2023) - [i17]Zakhar Shumaylov
Provably Convergent Data-Driven Convex-Nonconvex Regularization. CoRR abs/2310.05812 (2023) - [i16]Marcello Carioni, Subhadip Mukherjee, Hong Ye Tan
Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging. CoRR abs/2311.08972 (2023) - 2022
- [j6]Subhadip Mukherjee, Chandra Sekhar Seelamantula:
Quantization-aware phase retrieval. Int. J. Wavelets Multiresolution Inf. Process. 20(3): 2040006:1-2040006:43 (2022) - [c15]Subhadip Mukherjee
VCI Construction and Shifting Strategy Based Steganography for 3D Images. CICBA 2022: 210-219 - [c14]Arthur Conmy, Subhadip Mukherjee, Carola-Bibiane Schönlieb:
Stylegan-Induced Data-Driven Regularization for Inverse Problems. ICASSP 2022: 3788-3792 - [i15]Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem, Marcelo Pereyra, Carola-Bibiane Schönlieb:
Learned reconstruction with convergence guarantees. CoRR abs/2206.05431 (2022) - [i14]Debmita Bandyopadhyay, Subhadip Mukherjee:
Tree species classification from hyperspectral data using graph-regularized neural networks. CoRR abs/2208.08675 (2022) - [i13]Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb:
Accelerating Deep Unrolling Networks via Dimensionality Reduction. CoRR abs/2208.14784 (2022) - 2021
- [j5]Subhadip Mukherjee
Pencil shell matrix based image steganography with elevated embedding capacity. J. Inf. Secur. Appl. 62: 102955 (2021) - [c13]Subhadip Mukherjee, Marcello Carioni, Ozan Öktem, Carola-Bibiane Schönlieb:
End-to-end reconstruction meets data-driven regularization for inverse problems. NeurIPS 2021: 21413-21425 - [c12]Subhadip Mukherjee, Ozan Öktem, Carola-Bibiane Schönlieb:
Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems. SSVM 2021: 540-552 - [i12]Subhadip Mukherjee, Ozan Öktem, Carola-Bibiane Schönlieb:
Adversarially learned iterative reconstruction for imaging inverse problems. CoRR abs/2103.16151 (2021) - [i11]Subhadip Mukherjee, Marcello Carioni, Ozan Öktem, Carola-Bibiane Schönlieb:
End-to-end reconstruction meets data-driven regularization for inverse problems. CoRR abs/2106.03538 (2021) - [i10]Arthur Conmy, Subhadip Mukherjee, Carola-Bibiane Schönlieb:
StyleGAN-induced data-driven regularization for inverse problems. CoRR abs/2110.03814 (2021) - [i9]Subhadip Mukherjee, Carola-Bibiane Schönlieb, Martin Burger:
Learning convex regularizers satisfying the variational source condition for inverse problems. CoRR abs/2110.12520 (2021) - 2020
- [c11]Vinith Kishore, Subhadip Mukherjee, Chandra Sekhar Seelamantula:
PhaseSense - Signal Reconstruction from Phase-Only Measurements via Quadratic Programming. SPCOM 2020: 1-5 - [i8]Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb:
Learned convex regularizers for inverse problems. CoRR abs/2008.02839 (2020)
2010 – 2019
- 2019
- [j4]Subhadip Mukherjee
A Novel Method for High Capacity Reversible Data Hiding Scheme Using Difference Expansion. Int. J. Nat. Comput. Res. 8(4): 13-27 (2019) - 2018
- [j3]Subhadip Mukherjee
Phase Retrieval From Binary Measurements. IEEE Signal Process. Lett. 25(3): 348-352 (2018) - [c10]Subhadip Mukherjee, Suprosanna Shit, Chandra Sekhar Seelamantula:
Phasesplit: A Variable Splitting Framework for Phase Retrieval. ICASSP 2018: 4709-4713 - [c9]Jishnu Sadasivan, Subhadip Mukherjee, Chandra Sekhar Seelamantula:
Speech Enhancement Using the Minimum-probability-of-error Criterion. INTERSPEECH 2018: 1141-1145 - [c8]Subhadip Mukherjee, Anjany Kumar Sekuboyina, Chandra Sekhar Seelamantula:
Binary Compressive Sensing and Super-Resolution With Unknown Threshold. SPCOM 2018: 65-69 - [c7]Subhadip Mukherjee, Chandra Sekhar Seelamantula:
A Singular Value Relaxation Technique for Learning Sparsifying Transforms. SPCOM 2018: 70-74 - [i7]Subhadip Mukherjee, Chandra Sekhar Seelamantula:
Quantization-Aware Phase Retrieval. CoRR abs/1810.01097 (2018) - 2017
- [i6]Debabrata Mahapatra, Subhadip Mukherjee, Chandra Sekhar Seelamantula:
Deep Sparse Coding Using Optimized Linear Expansion of Thresholds. CoRR abs/1705.07290 (2017) - [i5]Subhadip Mukherjee, Deepak R., Huaijin G. Chen, Ashok Veeraraghavan, Chandra Sekhar Seelamantula:
Online Reweighted Least Squares Algorithm for Sparse Recovery and Application to Short-Wave Infrared Imaging. CoRR abs/1706.09585 (2017) - [i4]Subhadip Mukherjee, Chandra Sekhar Seelamantula:
Phase Retrieval From Binary Measurements. CoRR abs/1708.00602 (2017) - 2016
- [j2]Subhadip Mukherjee, Rupam Basu, Chandra Sekhar Seelamantula:
ℓ1-K-SVD: A robust dictionary learning algorithm with simultaneous update. Signal Process. 123: 42-52 (2016) - [c6]Subhadip Mukherjee, Chandra Sekhar Seelamantula:
A divide-and-conquer dictionary learning algorithm and its performance analysis. ICASSP 2016: 4712-4716 - [c5]Jishnu Sadasivan, Subhadip Mukherjee, Chandra Sekhar Seelamantula:
Joint dictionary training for bandwidth extension of speech signals. ICASSP 2016: 5925-5929 - [i3]Subhadip Mukherjee, Anjany Kumar Sekuboyina, Chandra Sekhar Seelamantula:
Super-Resolution From Binary Measurements With Unknown Threshold. CoRR abs/1606.03472 (2016) - 2014
- [j1]Subhadip Mukherjee, Chandra Sekhar Seelamantula:
Fienup Algorithm With Sparsity Constraints: Application to Frequency-Domain Optical-Coherence Tomography. IEEE Trans. Signal Process. 62(18): 4659-4672 (2014) - [c4]Jishnu Sadasivan, Subhadip Mukherjee, Chandra Sekhar Seelamantula:
An optimum shrinkage estimator based on minimum-probability-of-error criterion and application to signal denoising. ICASSP 2014: 4249-4253 - [c3]Subhadip Mukherjee, Chandra Sekhar Seelamantula:
A split-and-merge dictionary learning algorithm for sparse representation: Application to image denoising. DSP 2014: 310-315 - [i2]Subhadip Mukherjee, Chandra Sekhar Seelamantula:
A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation. CoRR abs/1403.4781 (2014) - [i1]Subhadip Mukherjee, Rupam Basu, Chandra Sekhar Seelamantula:
A Robust Dictionary Learning Algorithm for Image Denoising. CoRR abs/1410.0311 (2014) - 2013
- [c2]Basty Ajay Shenoy, Subhadip Mukherjee, Chandra Sekhar Seelamantula:
Phase retrieval for a class of 2-D signals characterized by first-order difference equations. ICIP 2013: 325-329 - 2012
- [c1]Subhadip Mukherjee, Chandra Sekhar Seelamantula:
An iterative algorithm for phase retrieval with sparsity constraints: application to frequency domain optical coherence tomography. ICASSP 2012: 553-556
Coauthor Index
aka: Carola B. Schönlieb
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