Fetching the paper…
Reading the bibliography…
An emerging new paradigm for solving inverse problems is via the use of deep learning to learn a regularizer from data.
On model-space and data-space regularization: A tutorial
Sergey Fomel · 1997
Earlier work this paper cites.
Image quality assessment: From error visibility to structural similarity
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
Earlier work this paper cites.
K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation
M. Aharon, M. Elad, and A. Bruckstein · 2006
Earlier work this paper cites.
A fast approach for overcomplete sparse decomposition based on smoothed ℓ 0 \ell^{0} norm
Hosein Mohimani, Massoud Babaie-Zadeh, and Christian Jutten · 2008
Earlier work this paper cites.
Learned convex regularizers for inverse problems
S. Mukherjee, S. Dittmer, Z. Shumaylov, S. Lunz, O. Öktem, and C.-B. Schönlieb · 2008
Earlier work this paper cites.
An overview on convergence rates for tikhonov regularization methods for non-linear operators
C. Pöschl · 2009
Earlier work this paper cites.
Fields of experts
Stefan Roth and Michael J Black · 2009
Earlier work this paper cites.
Plug-and-play priors for model based reconstruction
Singanallur V. Venkatakrishnan, Charles A. Bouman, and Brendt Wohlberg · 2013
Earlier work this paper cites.
Plug-and-play ADMM for image restoration: Fixed-point convergence and applications
Stanley H Chan, Xiran Wang, and Omar A Elgendy · 2016
Earlier work this paper cites.
TU-FG-207A-04: Overview of the Low Dose CT Grand Challenge
C. McCollough · 2016
Earlier work this paper cites.
Operator discretization library (ODL)
J. Adler, H. Kohr, and Ozan Öktem · 2017
Earlier work this paper cites.
Input convex neural networks
Brandon Amos, Lei Xu, and J Zico Kolter · 2017
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Deep convolutional neural network for inverse problems in imaging
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser · 2017
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
Cited alongside, same era.
The little engine that could: Regularization by denoising (RED)
Yaniv Romano, Michael Elad, and Peyman Milanfar · 2017
Cited alongside, same era.
Learned primal-dual reconstruction
Jonas Adler and Ozan Öktem · 2018
Cited alongside, same era.
Subgradient methods for sharp weakly convex functions
Damek Davis, Dmitriy Drusvyatskiy, Kellie J. MacPhee, and Courtney Paquette · 2018
Cited alongside, same era.
Total deep variation for linear inverse problems
Erich Kobler, Alexander Effland, Karl Kunisch, and Thomas Pock · 2020
Later among the works it cites.
NETT: solving inverse problems with deep neural networks
Housen Li, Johannes Schwab, Stephan Antholzer, and Markus Haltmeier · 2020
Later among the works it cites.
Unilateral variational analysis in Banach spaces
Lionel Thibault · 2021
Later among the works it cites.
Deep Generative Models and Inverse Problems , page 400–421
Alexandros G. Dimakis · 2022
Later among the works it cites.
Convex non-convex variational models
Alessandro Lanza, Serena Morigi, Ivan W Selesnick, and Fiorella Sgallari · 2022
Later among the works it cites.
Optimal convex and nonconvex regularizers for a data source, 2022
Oscar Leong, Eliza O’Reilly, Yong Sheng Soh, and Venkat Chandrasekaran · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adversarial regularizers in inverse problems
Sebastian Lunz, Ozan Öktem, and Carola-Bibiane Schönlieb · 2018
Cited alongside, same era.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
Cited alongside, same era.
Solving inverse problems using data-driven models
Simon Arridge, Peter Maass, Ozan Öktem, and Carola-Bibiane Schönlieb · 2019
Cited alongside, same era.
Auto-encoders for compressed sensing
Pei Peng, Shirin Jalali, and Xin Yuan · 2019
Cited alongside, same era.
Regularization by denoising: clarifications and new interpretations
E. T. Reehorst and P. Schniter · 2019
Cited alongside, same era.
Plug-and-play methods provably converge with properly trained denoisers
Ernest Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, and Wotao Yin · 2019
Cited alongside, same era.
A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applications, 2022
Tristan Milne, Étienne Bilocq, and Adrian Nachman · 2022
Later among the works it cites.
Learned reconstruction methods with convergence guarantees: A survey of concepts and applications
Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem, Marcelo Pereyra, and Carola-Bibiane Schönlieb · 2022
Later among the works it cites.
Nonconvex regularization for sparse neural networks
Konstantin Pieper and Armenak Petrosyan · 2022
Later among the works it cites.
Learning weakly convex regularizers for convergent image-reconstruction algorithms, 2023
Alexis Goujon, Sebastian Neumayer, and Michael Unser · 2023
Closest in time.
Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications
Ulugbek S Kamilov, Charles A Bouman, Gregery T Buzzard, and Brendt Wohlberg · 2023
Closest in time.
Convex latent-optimized adversarial regularizers for imaging inverse problems, 2023
Huayu Wang, Chen Luo, Taofeng Xie, Qiyu Jin, Guoqing Chen, Zhuo-Xu Cui, and Dong Liang · 2023
Closest in time.