End-to-end reconstruction meets data-driven regularization for inverse
problems

End-to-end reconstruction meets data-driven regularization for inverse problems

7 June 2021

Subhadip Mukherjee

Carola-Bibiane Schönlieb

Papers citing "End-to-end reconstruction meets data-driven regularization for inverse problems"

10 / 10 papers shown

Title
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping Xiaojian Xu Weijie Gan Satya V. V. N. Kothapalli D. Yablonskiy Ulugbek S. Kamilov MedIm 76 5 0 21 Feb 2025
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation Hongwei Tan Ziruo Cai Marcelo Pereyra Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb SSL 70 1 0 08 Apr 2024
What's in a Prior? Learned Proximal Networks for Inverse Problems Zhenghan Fang Sam Buchanan Jeremias Sulam 31 11 0 22 Oct 2023
A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applications Tristan Milne Étienne Bilocq A. Nachman OT 27 3 0 02 Nov 2022
Estimating a potential without the agony of the partition function E. Haber Moshe Eliasof L. Tenorio 33 2 0 19 Aug 2022
Stochastic Primal-Dual Deep Unrolling Junqi Tang Subhadip Mukherjee Carola-Bibiane Schönlieb 24 4 0 19 Oct 2021
Deep Bayesian inference for seismic imaging with tasks Ali Siahkoohi G. Rizzuti Felix J. Herrmann BDL UQCV 40 21 0 10 Oct 2021
Deep Unfolding Network for Image Super-Resolution Kaixuan Zhang Luc Van Gool Radu Timofte SupR 116 539 0 23 Mar 2020
Wasserstein GANs for MR Imaging: from Paired to Unpaired Training Ke Lei Morteza Mardani John M. Pauly S. Vasanawala GAN MedIm 38 64 0 15 Oct 2019
Regularization by Denoising: Clarifications and New Interpretations E. T. Reehorst Philip Schniter 56 214 0 06 Jun 2018