From the Gradient-Step Denoiser to the Proximal Denoiser and their associated convergent Plug-and-Play algorithms
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915491457531904 |
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| author | Herfeld, Vincent de Senneville, Baudouin Denis Leclaire, Arthur Papadakis, Nicolas |
| author_facet | Herfeld, Vincent de Senneville, Baudouin Denis Leclaire, Arthur Papadakis, Nicolas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09793 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From the Gradient-Step Denoiser to the Proximal Denoiser and their associated convergent Plug-and-Play algorithms Herfeld, Vincent de Senneville, Baudouin Denis Leclaire, Arthur Papadakis, Nicolas Machine Learning 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. |
| title | From the Gradient-Step Denoiser to the Proximal Denoiser and their associated convergent Plug-and-Play algorithms |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.09793 |