Single-Shot Plug-and-Play Methods for Inverse Problems
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866912113765646336 |
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| author | Cheng, Yanqi Zhang, Lipei Shen, Zhenda Wang, Shujun Yu, Lequan Chan, Raymond H. Schönlieb, Carola-Bibiane Aviles-Rivero, Angelica I |
| author_facet | Cheng, Yanqi Zhang, Lipei Shen, Zhenda Wang, Shujun Yu, Lequan Chan, Raymond H. Schönlieb, Carola-Bibiane Aviles-Rivero, Angelica I |
| contents | The utilisation of Plug-and-Play (PnP) priors in inverse problems has become increasingly prominent in recent years. This preference is based on the mathematical equivalence between the general proximal operator and the regularised denoiser, facilitating the adaptation of various off-the-shelf denoiser priors to a wide range of inverse problems. However, existing PnP models predominantly rely on pre-trained denoisers using large datasets. In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data. First, we integrate Single-Shot proximal denoisers into iterative methods, enabling training with single instances. Second, we propose implicit neural priors based on a novel function that preserves relevant frequencies to capture fine details while avoiding the issue of vanishing gradients. We demonstrate, through extensive numerical and visual experiments, that our method leads to better approximations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13682 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Single-Shot Plug-and-Play Methods for Inverse Problems Cheng, Yanqi Zhang, Lipei Shen, Zhenda Wang, Shujun Yu, Lequan Chan, Raymond H. Schönlieb, Carola-Bibiane Aviles-Rivero, Angelica I Computer Vision and Pattern Recognition Image and Video Processing The utilisation of Plug-and-Play (PnP) priors in inverse problems has become increasingly prominent in recent years. This preference is based on the mathematical equivalence between the general proximal operator and the regularised denoiser, facilitating the adaptation of various off-the-shelf denoiser priors to a wide range of inverse problems. However, existing PnP models predominantly rely on pre-trained denoisers using large datasets. In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data. First, we integrate Single-Shot proximal denoisers into iterative methods, enabling training with single instances. Second, we propose implicit neural priors based on a novel function that preserves relevant frequencies to capture fine details while avoiding the issue of vanishing gradients. We demonstrate, through extensive numerical and visual experiments, that our method leads to better approximations. |
| title | Single-Shot Plug-and-Play Methods for Inverse Problems |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2311.13682 |