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…
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…
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…
Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals
Hong Ye Tan, Stanley Osher, Wuchen Li
We consider the problem of sampling from a distribution governed by a potential function. This work proposes an explicit score based MCMC method that is deterministic, resulting in…
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…
Provably Convergent Plug-and-Play Quasi-Newton Methods
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as I…
Robust Data-Driven Accelerated Mirror Descent
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +2
Learning-to-optimize is an emerging framework that leverages training data to speed up the solution of certain optimization problems. One such approach is based on the classical mi…
Data-Driven Mirror Descent with Input-Convex Neural Networks
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Learning-to-optimize is an emerging framework that seeks to speed up the solution of certain optimization problems by leveraging training data. Learned optimization solvers have be…