Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866929352744108032 |
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| author | Laroche, Charles Almansa, Andrés Coupeté, Eva Tassano, Matias |
| author_facet | Laroche, Charles Almansa, Andrés Coupeté, Eva Tassano, Matias |
| contents | Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems. These methods rely on the computability of the proximal operator of the data fidelity term. In this paper, we propose a Plug & Play framework based on linearized ADMM that allows us to bypass the computation of intractable proximal operators. We demonstrate the convergence of the algorithm and provide results on restoration tasks such as super-resolution and deblurring with non-uniform blur. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_10605 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels Laroche, Charles Almansa, Andrés Coupeté, Eva Tassano, Matias Computer Vision and Pattern Recognition Optimization and Control Applications Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems. These methods rely on the computability of the proximal operator of the data fidelity term. In this paper, we propose a Plug & Play framework based on linearized ADMM that allows us to bypass the computation of intractable proximal operators. We demonstrate the convergence of the algorithm and provide results on restoration tasks such as super-resolution and deblurring with non-uniform blur. |
| title | Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels |
| topic | Computer Vision and Pattern Recognition Optimization and Control Applications |
| url | https://arxiv.org/abs/2210.10605 |