mlsanityData to 7 Oct 2026

Learning local regularization for variational image restoration

arXiv 2102.06155 ↗First posted 11 Feb 2021eess.IV cs.LGcited by 16

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Abstract (arXiv)

In this work, we propose a framework to learn a local regularization model for solving general image restoration problems. This regularizer is defined with a fully convolutional neural network that sees the image through a receptive field corresponding to small image patches. The regularizer is then learned as a critic between unpaired distributions of clean and degraded patches using a Wasserstein generative adversarial networks based energy. This yields a regularization function that can be incorporated in any image restoration problem. The efficiency of the framework is finally shown on denoising and deblurring applications.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 11 Feb 2021.

Builds on

14 of 28 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2017Improved Training of Wasserstein GANscited by 2,223
  4. 2017Learning Deep CNN Denoiser Prior for Image Restorationcited by 407
  5. 2017Compressed Sensing using Generative Modelscited by 337
  6. 2020Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrievalcited by 305
  7. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 303
  8. 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 197
  9. 2017Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problemscited by 107
  10. 2018Adversarial Regularizers in Inverse Problemscited by 102
  11. 2018Regularization by Denoising: Clarifications and New Interpretationscited by 102
  12. 2017Deep Mean-Shift Priors for Image Restorationcited by 34
2 more
  1. 2019Solving Inverse Problems by Joint Posterior Maximization with a VAE Priorcited by 6
  2. 2018Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imagingcited by 5

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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 6; 2024: 3; 2025: 3.

16 papers.

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  1. 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 107
  2. 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
  3. 2022PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularizationcited by 22
  4. 2025Learning Regularization Functionals for Inverse Problems: A Comparative Studycited by 12
  5. 2023Learning from small data sets: Patch-based regularizers in inverse problems for image reconstructioncited by 9
  6. 2023Inverse problem regularization with hierarchical variational autoencoderscited by 6
  7. 2025DEALing with Image Reconstruction: Deep Attentive Least Squarescited by 6
  8. 2024Stability of Data-Dependent Ridge-Regularization for Inverse Problemscited by 5
  9. 2023NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problemscited by 4
  10. 2021Designing Rotationally Invariant Neural Networks from PDEs and Variational Methodscited by 3
  11. 2023Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problemscited by 3
  12. 2023Convergence Guarantees of Overparametrized Wide Deep Inverse Priorcited by 2
4 more
  1. 2024A primal-dual algorithm for image reconstruction with input-convex neural network regularizerscited by 2
  2. 2024Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overviewcited by 1
  3. 2025PAC Learning is just Bipartite Matching (Sort of)cited by 1
  4. 2023Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoisingcited by 0