From the Gradient-Step Denoiser to the Proximal Denoiser and their associated convergent Plug-and-Play algorithms
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Abstract (arXiv)
In this paper we analyze the Gradient-Step Denoiser and its usage in Plug-and-Play algorithms. The Plug-and-Play paradigm of optimization algorithms uses off the shelf denoisers to replace a proximity operator or a gradient descent operator of an image prior. Usually this image prior is implicit and cannot be expressed, but the Gradient-Step Denoiser is trained to be exactly the gradient descent operator or the proximity operator of an explicit functional while preserving state-of-the-art denoising capabilities.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 11 Sep 2025.
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References · 13
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
- 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 470
- 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 307
- 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 198
- 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 108
- 2018Regularization by Denoising: Clarifications and New Interpretationscited by 103
- 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 80
- 2020Learning Maximally Monotone Operators for Image Recoverycited by 62
- 2020Scalable Plug-and-Play ADMM with Convergence Guaranteescited by 50
- 2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
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