Constrained Regularization by Denoising with Automatic Parameter Selection
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866929207382114304 |
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| author | Cascarano, Pasquale Benfenati, Alessandro Kamilov, Ulugbek S. Xu, Xiaojian |
| author_facet | Cascarano, Pasquale Benfenati, Alessandro Kamilov, Ulugbek S. Xu, Xiaojian |
| contents | Regularization by Denoising (RED) is a well-known method for solving image restoration problems by using learned image denoisers as priors. Since the regularization parameter in the traditional RED does not have any physical interpretation, it does not provide an approach for automatic parameter selection. This letter addresses this issue by introducing the Constrained Regularization by Denoising (CRED) method that reformulates RED as a constrained optimization problem where the regularization parameter corresponds directly to the amount of noise in the measurements. The solution to the constrained problem is solved by designing an efficient method based on alternating direction method of multipliers (ADMM). Our experiments show that CRED outperforms the competing methods in terms of stability and robustness, while also achieving competitive performances in terms of image quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_03819 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Constrained Regularization by Denoising with Automatic Parameter Selection Cascarano, Pasquale Benfenati, Alessandro Kamilov, Ulugbek S. Xu, Xiaojian Optimization and Control Regularization by Denoising (RED) is a well-known method for solving image restoration problems by using learned image denoisers as priors. Since the regularization parameter in the traditional RED does not have any physical interpretation, it does not provide an approach for automatic parameter selection. This letter addresses this issue by introducing the Constrained Regularization by Denoising (CRED) method that reformulates RED as a constrained optimization problem where the regularization parameter corresponds directly to the amount of noise in the measurements. The solution to the constrained problem is solved by designing an efficient method based on alternating direction method of multipliers (ADMM). Our experiments show that CRED outperforms the competing methods in terms of stability and robustness, while also achieving competitive performances in terms of image quality. |
| title | Constrained Regularization by Denoising with Automatic Parameter Selection |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2311.03819 |