Total Deep Variation for Linear Inverse Problems

14 January 2020

Papers citing "Total Deep Variation for Linear Inverse Problems"

23 / 23 papers shown

Title
Good Things Come in Pairs: Paired Autoencoders for Inverse Problems Matthias Chung Bas Peters Michael Solomon 34 0 0 10 May 2025
Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes [conference paper] Andreas Habring Alexander Falk Thomas Pock 60 0 0 03 Feb 2025
Stability of Data-Dependent Ridge-Regularization for Inverse Problems Sebastian Neumayer Fabian Altekrüger 42 1 0 18 Jun 2024
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation Hongwei Tan Ziruo Cai Marcelo Pereyra Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb SSL 73 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
Neural Gradient Regularizer Shuang Xu Yifan Wang Zixiang Zhao Jiangjun Peng Xiangyong Cao Deyu Meng Yulun Zhang Radu Timofte Luc Van Gool 26 0 0 31 Aug 2023
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms Alexis Goujon Sebastian Neumayer M. Unser 41 23 0 21 Aug 2023
Deep unfolding as iterative regularization for imaging inverse problems Zhuoxu Cui Qingyong Zhu Jing Cheng Dong Liang 34 5 0 24 Nov 2022
A Neural-Network-Based Convex Regularizer for Inverse Problems Alexis Goujon Sebastian Neumayer Pakshal Bohra Stanislas Ducotterd M. Unser 19 26 0 22 Nov 2022
Stable Deep MRI Reconstruction using Generative Priors Martin Zach Florian Knoll Thomas Pock OOD MedIm DiffM 31 17 0 25 Oct 2022
Explainable bilevel optimization: an application to the Helsinki deblur challenge S. Bonettini Giorgia Franchini Danilo Pezzi M. Prato 19 10 0 18 Oct 2022
Learning Variational Models with Unrolling and Bilevel Optimization Christoph Brauer Niklas Breustedt T. Wolff D. Lorenz SSL 34 3 0 26 Sep 2022
Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions Sheheryar Mehmood Peter Ochs 33 3 0 05 Aug 2022
Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields Ronan Fablet Bertrand Chapron 18 4 0 20 Mar 2022
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution Fabian Altekrüger J. Hertrich 27 15 0 20 Jan 2022
Supervised learning of analysis-sparsity priors with automatic differentiation Hashem Ghanem Joseph Salmon Nicolas Keriven Samuel Vaiter 38 2 0 15 Dec 2021
Generalized Normalizing Flows via Markov Chains Paul Hagemann J. Hertrich Gabriele Steidl BDL DiffM AI4CE 30 22 0 24 Nov 2021
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods Tobias Alt Karl Schrader Joachim Weickert Pascal Peter M. Augustin 22 4 0 31 Aug 2021
Learning the optimal Tikhonov regularizer for inverse problems Giovanni S. Alberti E. De Vito Matti Lassas Luca Ratti Matteo Santacesaria 25 30 0 11 Jun 2021
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie R. Laumont Valentin De Bortoli Andrés Almansa J. Delon Alain Durmus Marcelo Pereyra 24 109 0 08 Mar 2021
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction Dominik Narnhofer Alexander Effland Erich Kobler Kerstin Hammernik Florian Knoll Thomas Pock UQCV BDL 23 49 0 12 Feb 2021
Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction Yunmei Chen Hongcheng Liu X. Ye Qingchao Zhang 56 23 0 22 Jul 2020
Total Deep Variation: A Stable Regularizer for Inverse Problems Erich Kobler Alexander Effland K. Kunisch Thomas Pock MedIm 27 19 0 15 Jun 2020