An Image Segmentation Model with Transformed Total Variation
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866916272432742400 |
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| author | Dayag, Elisha Bui, Kevin Park, Fredrick Xin, Jack |
| author_facet | Dayag, Elisha Bui, Kevin Park, Fredrick Xin, Jack |
| contents | Based on transformed $\ell_1$ regularization, transformed total variation (TTV) has robust image recovery that is competitive with other nonconvex total variation (TV) regularizers, such as TV$^p$, $0<p<1$. Inspired by its performance, we propose a TTV-regularized Mumford--Shah model with fuzzy membership function for image segmentation. To solve it, we design an alternating direction method of multipliers (ADMM) algorithm that utilizes the transformed $\ell_1$ proximal operator. Numerical experiments demonstrate that using TTV is more effective than classical TV and other nonconvex TV variants in image segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00571 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Image Segmentation Model with Transformed Total Variation Dayag, Elisha Bui, Kevin Park, Fredrick Xin, Jack Computer Vision and Pattern Recognition Numerical Analysis Image and Video Processing Based on transformed $\ell_1$ regularization, transformed total variation (TTV) has robust image recovery that is competitive with other nonconvex total variation (TV) regularizers, such as TV$^p$, $0<p<1$. Inspired by its performance, we propose a TTV-regularized Mumford--Shah model with fuzzy membership function for image segmentation. To solve it, we design an alternating direction method of multipliers (ADMM) algorithm that utilizes the transformed $\ell_1$ proximal operator. Numerical experiments demonstrate that using TTV is more effective than classical TV and other nonconvex TV variants in image segmentation. |
| title | An Image Segmentation Model with Transformed Total Variation |
| topic | Computer Vision and Pattern Recognition Numerical Analysis Image and Video Processing |
| url | https://arxiv.org/abs/2406.00571 |