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Learning Weakly Convex Regularizers for Convergent Image-Reconstruction
  Algorithms

Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms

21 August 2023
Alexis Goujon
Sebastian Neumayer
M. Unser
ArXivPDFHTML

Papers citing "Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms"

14 / 14 papers shown
Title
VibrantLeaves: A principled parametric image generator for training deep restoration models
VibrantLeaves: A principled parametric image generator for training deep restoration models
Raphaël Achddou
Y. Gousseau
Saïd Ladjal
Sabine Süsstrunk
23
0
0
14 Apr 2025
Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers
Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers
M. Unser
Stanislas Ducotterd
35
0
0
24 Mar 2025
Gradient Networks
Gradient Networks
Shreyas Chaudhari
Srinivasa Pranav
J. M. F. Moura
50
0
0
28 Jan 2025
The Star Geometry of Critic-Based Regularizer Learning
The Star Geometry of Critic-Based Regularizer Learning
Oscar Leong
Eliza O'Reilly
Yong Sheng Soh
AAML
37
0
0
29 Aug 2024
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures
Michael Unser
Alexis Goujon
Stanislas Ducotterd
24
2
0
23 Aug 2024
Iteratively Refined Image Reconstruction with Learned Attentive
  Regularizers
Iteratively Refined Image Reconstruction with Learned Attentive Regularizers
Mehrsa Pourya
Sebastian Neumayer
Michael Unser
27
0
0
09 Jul 2024
Stability of Data-Dependent Ridge-Regularization for Inverse Problems
Stability of Data-Dependent Ridge-Regularization for Inverse Problems
Sebastian Neumayer
Fabian Altekrüger
29
1
0
18 Jun 2024
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical
  Points and Primal-Dual Optimisation
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Zakhar Shumaylov
Jeremy Budd
Subhadip Mukherjee
Carola-Bibiane Schönlieb
21
5
0
01 Feb 2024
What's in a Prior? Learned Proximal Networks for Inverse Problems
What's in a Prior? Learned Proximal Networks for Inverse Problems
Zhenghan Fang
Sam Buchanan
Jeremias Sulam
23
11
0
22 Oct 2023
Provably Convergent Data-Driven Convex-Nonconvex Regularization
Provably Convergent Data-Driven Convex-Nonconvex Regularization
Zakhar Shumaylov
Jeremy Budd
Subhadip Mukherjee
Carola-Bibiane Schönlieb
30
5
0
09 Oct 2023
Asynchronous Multi-Model Dynamic Federated Learning over Wireless
  Networks: Theory, Modeling, and Optimization
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization
Zhangyu Chang
Seyyedali Hosseinalipour
M. Chiang
Christopher G. Brinton
21
3
0
22 May 2023
Provably Convergent Plug-and-Play Quasi-Newton Methods
Provably Convergent Plug-and-Play Quasi-Newton Methods
Hongwei Tan
Subhadip Mukherjee
Junqi Tang
Carola-Bibiane Schönlieb
26
13
0
09 Mar 2023
Improving Lipschitz-Constrained Neural Networks by Learning Activation
  Functions
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Stanislas Ducotterd
Alexis Goujon
Pakshal Bohra
Dimitris Perdios
Sebastian Neumayer
M. Unser
35
12
0
28 Oct 2022
Proximal Denoiser for Convergent Plug-and-Play Optimization with
  Nonconvex Regularization
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Samuel Hurault
Arthur Leclaire
Nicolas Papadakis
45
70
0
31 Jan 2022
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