Learning Convex Regularizers for Optimal Bayesian Denoising

16 May 2017

Papers citing "Learning Convex Regularizers for Optimal Bayesian Denoising"

8 / 8 papers shown

Title
Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers M. Unser Stanislas Ducotterd 112 0 0 24 Mar 2025
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures Michael Unser Alexis Goujon Stanislas Ducotterd 120 2 0 23 Aug 2024
Provably Convergent Plug-and-Play Quasi-Newton Methods Hongwei Tan Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb 84 15 0 09 Mar 2023
A Neural-Network-Based Convex Regularizer for Inverse Problems Alexis Goujon Sebastian Neumayer Pakshal Bohra Stanislas Ducotterd M. Unser 95 27 0 22 Nov 2022
Supervised Learning of Sparsity-Promoting Regularizers for Denoising Michael T. McCann S. Ravishankar 47 8 0 09 Jun 2020
Image Speckle Noise Denoising by a Multi-Layer Fusion Enhancement Method based on Block Matching and 3D Filtering Shuo Huang Ping Zhou Hao Shi Yu Sun S. Wan 3DV 25 16 0 04 Jan 2020
Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging Michael R. Kellman E. Bostan N. Repina Laura Waller 124 125 0 10 Aug 2018
Learning-based Image Reconstruction via Parallel Proximal Algorithm E. Bostan Ulugbek S. Kamilov Laura Waller 43 16 0 29 Jan 2018