16 citations · 74 across the 31 of their papers we have counts for
9 papers · 1 filter
Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging
Marcello Carioni, Subhadip Mukherjee, Hong Ye Tan +1
Unsupervised deep learning approaches have recently become one of the crucial research areas in imaging owing to their ability to learn expressive and powerful reconstruction opera…
Provably Convergent Data-Driven Convex-Nonconvex Regularization
Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee +1
An emerging new paradigm for solving inverse problems is via the use of deep learning to learn a regularizer from data. This leads to high-quality results, but often at the cost of…
An adaptively inexact first-order method for bilevel optimization with application to hyperparameter learning
Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts +1
Various tasks in data science are modeled utilizing the variational regularization approach, where manually selecting regularization parameters presents a challenge. The difficulty…
Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Learning-to-optimize (L2O) is an emerging research area in large-scale optimization with applications in data science. Recently, researchers have proposed a novel L2O framework cal…
Convergent regularization in inverse problems and linear plug-and-play denoisers
Andreas Hauptmann, Subhadip Mukherjee, Carola-Bibiane Schönlieb +1
Plug-and-play (PnP) denoising is a popular iterative framework for solving imaging inverse problems using off-the-shelf image denoisers. Their empirical success has motivated a lin…
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
Ziruo Cai, Junqi Tang, Subhadip Mukherjee +3
Bayesian methods for solving inverse problems are a powerful alternative to classical methods since the Bayesian approach offers the ability to quantify the uncertainty in the solu…