Computationally iterative methods for salt-and-pepper denoising
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912324808343552 |
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| author | Ke, Jianwei |
| author_facet | Ke, Jianwei |
| contents | Image restoration refers to the process of reconstructing noisy, destroyed, or missing parts of an image, which is an ill-posed inverse problem. A specific regularization term and image degradation are typically assumed to achieve well-posedness. Based on the underlying assumption, an image restoration problem can be modeled as a linear or non-linear optimization problem with or without regularization, which can be solved by iterative methods. In this work, we propose two different iterative methods by linearizing a system of non-linear equations and coupling them with a two-phase iterative framework. The qualitative and quantitative experimental results demonstrate the correctness and efficiency of the proposed methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computationally iterative methods for salt-and-pepper denoising Ke, Jianwei Image and Video Processing Image restoration refers to the process of reconstructing noisy, destroyed, or missing parts of an image, which is an ill-posed inverse problem. A specific regularization term and image degradation are typically assumed to achieve well-posedness. Based on the underlying assumption, an image restoration problem can be modeled as a linear or non-linear optimization problem with or without regularization, which can be solved by iterative methods. In this work, we propose two different iterative methods by linearizing a system of non-linear equations and coupling them with a two-phase iterative framework. The qualitative and quantitative experimental results demonstrate the correctness and efficiency of the proposed methods. |
| title | Computationally iterative methods for salt-and-pepper denoising |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2504.09408 |