Equivariant Denoisers for Image Restoration
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910849341325312 |
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| author | Renaud, Marien Leclaire, Arthur Papadakis, Nicolas |
| author_facet | Renaud, Marien Leclaire, Arthur Papadakis, Nicolas |
| contents | One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Moreover, typical image distributions are invariant to some set of transformations, such as rotations or flips. However, most deep architectures are not designed to represent an invariant image distribution. Recent works have proposed to overcome this difficulty by including equivariance properties within a Plug-and-Play paradigm. In this work, we propose a unified framework named Equivariant Regularization by Denoising (ERED) based on equivariant denoisers and stochastic optimization. We analyze the convergence of this algorithm and discuss its practical benefit. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_05343 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Equivariant Denoisers for Image Restoration Renaud, Marien Leclaire, Arthur Papadakis, Nicolas Image and Video Processing Computer Vision and Pattern Recognition Machine Learning One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Moreover, typical image distributions are invariant to some set of transformations, such as rotations or flips. However, most deep architectures are not designed to represent an invariant image distribution. Recent works have proposed to overcome this difficulty by including equivariance properties within a Plug-and-Play paradigm. In this work, we propose a unified framework named Equivariant Regularization by Denoising (ERED) based on equivariant denoisers and stochastic optimization. We analyze the convergence of this algorithm and discuss its practical benefit. |
| title | Equivariant Denoisers for Image Restoration |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2412.05343 |