An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929710342078464 |
|---|---|
| author | Wang, Qingsong Xu, Shengze Tong, Xiaojiao Zeng, Tieyong |
| author_facet | Wang, Qingsong Xu, Shengze Tong, Xiaojiao Zeng, Tieyong |
| contents | Image deblurring remains a central research area within image processing, critical for its role in enhancing image quality and facilitating clearer visual representations across diverse applications. This paper tackles the optimization problem of image deblurring, assuming a known blurring kernel. We introduce an improved optimal proximal gradient algorithm (IOptISTA), which builds upon the optimal gradient method and a weighting matrix, to efficiently address the non-blind image deblurring problem. Based on two regularization cases, namely the $l_1$ norm and total variation norm, we perform numerical experiments to assess the performance of our proposed algorithm. The results indicate that our algorithm yields enhanced PSNR and SSIM values, as well as a reduced tolerance, compared to existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07602 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring Wang, Qingsong Xu, Shengze Tong, Xiaojiao Zeng, Tieyong Computer Vision and Pattern Recognition Optimization and Control Image deblurring remains a central research area within image processing, critical for its role in enhancing image quality and facilitating clearer visual representations across diverse applications. This paper tackles the optimization problem of image deblurring, assuming a known blurring kernel. We introduce an improved optimal proximal gradient algorithm (IOptISTA), which builds upon the optimal gradient method and a weighting matrix, to efficiently address the non-blind image deblurring problem. Based on two regularization cases, namely the $l_1$ norm and total variation norm, we perform numerical experiments to assess the performance of our proposed algorithm. The results indicate that our algorithm yields enhanced PSNR and SSIM values, as well as a reduced tolerance, compared to existing methods. |
| title | An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring |
| topic | Computer Vision and Pattern Recognition Optimization and Control |
| url | https://arxiv.org/abs/2502.07602 |