An Improved Variational Method for Image Denoising
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909334596747264 |
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| author | Huang, Jing-En Liao, Jia-Wei Lin, Ku-Te Tsai, Yu-Ju Yueh, Mei-Heng |
| author_facet | Huang, Jing-En Liao, Jia-Wei Lin, Ku-Te Tsai, Yu-Ju Yueh, Mei-Heng |
| contents | The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of the image, which measures the variation in pixel intensities. The TV method has been widely applied in image processing and computer vision for its ability to preserve edges and enhance image quality. In this paper, we propose an improved TV model for image denoising and the associated numerical algorithm to carry out the procedure, which is particularly effective in removing several types of noises and their combinations. Our improved model admits a unique solution and the associated numerical algorithm guarantees the convergence. Numerical experiments are demonstrated to show improved effectiveness and denoising quality compared to other TV models. Such encouraging results further enhance the utility of the TV method in image processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02587 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Improved Variational Method for Image Denoising Huang, Jing-En Liao, Jia-Wei Lin, Ku-Te Tsai, Yu-Ju Yueh, Mei-Heng Computer Vision and Pattern Recognition Numerical Analysis The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of the image, which measures the variation in pixel intensities. The TV method has been widely applied in image processing and computer vision for its ability to preserve edges and enhance image quality. In this paper, we propose an improved TV model for image denoising and the associated numerical algorithm to carry out the procedure, which is particularly effective in removing several types of noises and their combinations. Our improved model admits a unique solution and the associated numerical algorithm guarantees the convergence. Numerical experiments are demonstrated to show improved effectiveness and denoising quality compared to other TV models. Such encouraging results further enhance the utility of the TV method in image processing. |
| title | An Improved Variational Method for Image Denoising |
| topic | Computer Vision and Pattern Recognition Numerical Analysis |
| url | https://arxiv.org/abs/2410.02587 |