13 citations · 25 across the 7 of their papers we have counts for
5 papers · 1 filter
Learning convex regularizers satisfying the variational source condition for inverse problems
Subhadip Mukherjee, Carola-Bibiane Schönlieb, Martin Burger
Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades. With the emergence and astonishing…
Learned convex regularizers for inverse problems
Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov +3
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.…
Quantization-Aware Phase Retrieval
Subhadip Mukherjee, Chandra Sekhar Seelamantula
We address the problem of phase retrieval (PR) from quantized measurements. The goal is to reconstruct a signal from quadratic measurements encoded with a finite precision, which i…
Online Reweighted Least Squares Algorithm for Sparse Recovery and Application to Short-Wave Infrared Imaging
Subhadip Mukherjee, Deepak R., Huaijin Chen +2
We address the problem of sparse recovery in an online setting, where random linear measurements of a sparse signal are revealed sequentially and the objective is to recover the un…
Deep Sparse Coding Using Optimized Linear Expansion of Thresholds
Debabrata Mahapatra, Subhadip Mukherjee, Chandra Sekhar Seelamantula
We address the problem of reconstructing sparse signals from noisy and compressive measurements using a feed-forward deep neural network (DNN) with an architecture motivated by the…