1 citations · 1 across the 8 of their papers we have counts for
8 papers
Solving Imaging Inverse Problems Using Plug-and-Play Denoisers: Regularization and Optimization Perspectives
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang
Inverse problems lie at the heart of modern imaging science, with broad applications in areas such as medical imaging, remote sensing, and microscopy. Recent years have witnessed a…
Blessing of Dimensionality for Approximating Sobolev Classes on Manifolds
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
The manifold hypothesis says that natural high-dimensional data lie on or around a low-dimensional manifold. The recent success of statistical and learning-based methods in very hi…
A Guide to Stochastic Optimisation for Large-Scale Inverse Problems
Matthias J. Ehrhardt, Zeljko Kereta, Jingwei Liang +1
Stochastic optimisation algorithms are the de facto standard for machine learning with large amounts of data. Handling only a subset of available data in each optimisation step dra…
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation
Hong Ye Tan, Ziruo Cai, Marcelo Pereyra +3
Imaging is a standard example of an inverse problem, where the task of reconstructing a ground truth from a noisy measurement is ill-posed. Recent state-of-the-art approaches for i…
Practical Acceleration of the Condat-Vũ Algorithm
Derek Driggs, Matthias J. Ehrhardt, Carola-Bibiane Schönlieb +1
The Condat-Vũ algorithm is a widely used primal-dual method for optimizing composite objectives of three functions. Several algorithms for optimizing composite objectives of two fu…
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…
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…
Stochastic Primal-Dual Three Operator Splitting Algorithm with Extension to Equivariant Regularization-by-Denoising
Junqi Tang, Matthias Ehrhardt, Carola-Bibiane Schönlieb
In this work we propose a stochastic primal-dual three-operator splitting algorithm (TOS-SPDHG) for solving a class of convex three-composite optimization problems. Our proposed sc…