Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm
Image deblurring based on sparse regularization has garnered significant attention, but there are still certain limitations that need to be addressed. For instance, convex sparse regularization tends to exhibit biased estimation, which can adversely impact the deblurring performance, while non-conve...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/12/574 |
| _version_ | 1827575908845748224 |
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| author | Yi Wang Yating Xu Tianjian Li Tao Zhang Jian Zou |
| author_facet | Yi Wang Yating Xu Tianjian Li Tao Zhang Jian Zou |
| author_sort | Yi Wang |
| collection | DOAJ |
| description | Image deblurring based on sparse regularization has garnered significant attention, but there are still certain limitations that need to be addressed. For instance, convex sparse regularization tends to exhibit biased estimation, which can adversely impact the deblurring performance, while non-convex sparse regularization poses challenges in terms of solving techniques. Furthermore, the performance of the traditional iterative algorithm also needs to be improved. In this paper, we propose an image deblurring method based on convex non-convex (CNC) sparse regularization and a plug-and-play (PnP) algorithm. The utilization of CNC sparse regularization not only mitigates estimation bias but also guarantees the overall convexity of the image deblurring model. The PnP algorithm is an advanced learning-based optimization algorithm that surpasses traditional optimization algorithms in terms of efficiency and performance by utilizing the state-of-the-art denoiser to replace the proximal operator. Numerical experiments verify the performance of our proposed algorithm in image deblurring. |
| first_indexed | 2024-03-08T21:03:38Z |
| format | Article |
| id | doaj.art-7d03a673fbaf491ebdb351be62da80ba |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-08T21:03:38Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-7d03a673fbaf491ebdb351be62da80ba2023-12-22T13:47:07ZengMDPI AGAlgorithms1999-48932023-12-01161257410.3390/a16120574Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play AlgorithmYi Wang0Yating Xu1Tianjian Li2Tao Zhang3Jian Zou4School of Information and Mathematics, Yangtze University, Jingzhou 434020, ChinaSchool of Information and Mathematics, Yangtze University, Jingzhou 434020, ChinaSchool of Information and Mathematics, Yangtze University, Jingzhou 434020, ChinaSchool of Information and Mathematics, Yangtze University, Jingzhou 434020, ChinaSchool of Information and Mathematics, Yangtze University, Jingzhou 434020, ChinaImage deblurring based on sparse regularization has garnered significant attention, but there are still certain limitations that need to be addressed. For instance, convex sparse regularization tends to exhibit biased estimation, which can adversely impact the deblurring performance, while non-convex sparse regularization poses challenges in terms of solving techniques. Furthermore, the performance of the traditional iterative algorithm also needs to be improved. In this paper, we propose an image deblurring method based on convex non-convex (CNC) sparse regularization and a plug-and-play (PnP) algorithm. The utilization of CNC sparse regularization not only mitigates estimation bias but also guarantees the overall convexity of the image deblurring model. The PnP algorithm is an advanced learning-based optimization algorithm that surpasses traditional optimization algorithms in terms of efficiency and performance by utilizing the state-of-the-art denoiser to replace the proximal operator. Numerical experiments verify the performance of our proposed algorithm in image deblurring.https://www.mdpi.com/1999-4893/16/12/574image deblurringplug-and-play algorithmconvex non-convex strategysparse regularization |
| spellingShingle | Yi Wang Yating Xu Tianjian Li Tao Zhang Jian Zou Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm Algorithms image deblurring plug-and-play algorithm convex non-convex strategy sparse regularization |
| title | Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm |
| title_full | Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm |
| title_fullStr | Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm |
| title_full_unstemmed | Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm |
| title_short | Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm |
| title_sort | image deblurring based on convex non convex sparse regularization and plug and play algorithm |
| topic | image deblurring plug-and-play algorithm convex non-convex strategy sparse regularization |
| url | https://www.mdpi.com/1999-4893/16/12/574 |
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