Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916634753499136 |
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| author | Renaud, Marien Hermant, Julien Papadakis, Nicolas |
| author_facet | Renaud, Marien Hermant, Julien Papadakis, Nicolas |
| contents | Plug-and-Play methods for image restoration are iterative algorithms that solve a variational problem to recover a clean image from a degraded observation. These algorithms are known to be flexible to changes of degradation and to perform state-of-the-art restoration. Recently, significant efforts have been made to explore new stochastic algorithms based on the Plug-and-Play or REgularization by Denoising (RED) frameworks, such as SNORE, which is a convergent stochastic gradient descent algorithm. A variant of this algorithm, named SNORE Prox, reaches state-of-the-art performances, especially for inpainting tasks. However, the convergence of SNORE Prox, that can be seen as a stochastic proximal gradient descent, has not been analyzed so far. In this paper, we prove the convergence of SNORE Prox under non convex assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08262 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm Renaud, Marien Hermant, Julien Papadakis, Nicolas Image and Video Processing Plug-and-Play methods for image restoration are iterative algorithms that solve a variational problem to recover a clean image from a degraded observation. These algorithms are known to be flexible to changes of degradation and to perform state-of-the-art restoration. Recently, significant efforts have been made to explore new stochastic algorithms based on the Plug-and-Play or REgularization by Denoising (RED) frameworks, such as SNORE, which is a convergent stochastic gradient descent algorithm. A variant of this algorithm, named SNORE Prox, reaches state-of-the-art performances, especially for inpainting tasks. However, the convergence of SNORE Prox, that can be seen as a stochastic proximal gradient descent, has not been analyzed so far. In this paper, we prove the convergence of SNORE Prox under non convex assumptions. |
| title | Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2412.08262 |