An Efficient Variational Method for Image Restoration
Image restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its capability to preserve edges. In this paper, we consider a constrained minimization problem with double total variation regularization terms...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2013/213536 |
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| _version_ | 1832547588941807616 |
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| author | Jun Liu Ting-Zhu Huang Xiao-Guang Lv Si Wang |
| author_facet | Jun Liu Ting-Zhu Huang Xiao-Guang Lv Si Wang |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | Image restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its capability to preserve edges. In this paper, we consider a constrained minimization problem with double total variation regularization terms. To solve this problem, we employ the split Bregman iteration method and the Chambolle’s algorithm. The convergence property of the algorithm is established. The numerical results demonstrate the effectiveness of the proposed method in terms of peak signal-to-noise ratio (PSNR) and the structure similarity index (SSIM). |
| format | Article |
| id | doaj-art-94af222bb58c4cd38a78ba03a5d038bb |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-94af222bb58c4cd38a78ba03a5d038bb2025-02-03T06:44:16ZengWileyAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/213536213536An Efficient Variational Method for Image RestorationJun Liu0Ting-Zhu Huang1Xiao-Guang Lv2Si Wang3School of Mathematical Sciences/Institute of Computational Science, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, ChinaSchool of Mathematical Sciences/Institute of Computational Science, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, ChinaSchool of Science, Huaihai Institute of Technology, Lianyungang, Jiangsu 222005, ChinaSchool of Mathematical Sciences/Institute of Computational Science, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, ChinaImage restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its capability to preserve edges. In this paper, we consider a constrained minimization problem with double total variation regularization terms. To solve this problem, we employ the split Bregman iteration method and the Chambolle’s algorithm. The convergence property of the algorithm is established. The numerical results demonstrate the effectiveness of the proposed method in terms of peak signal-to-noise ratio (PSNR) and the structure similarity index (SSIM).http://dx.doi.org/10.1155/2013/213536 |
| spellingShingle | Jun Liu Ting-Zhu Huang Xiao-Guang Lv Si Wang An Efficient Variational Method for Image Restoration Abstract and Applied Analysis |
| title | An Efficient Variational Method for Image Restoration |
| title_full | An Efficient Variational Method for Image Restoration |
| title_fullStr | An Efficient Variational Method for Image Restoration |
| title_full_unstemmed | An Efficient Variational Method for Image Restoration |
| title_short | An Efficient Variational Method for Image Restoration |
| title_sort | efficient variational method for image restoration |
| url | http://dx.doi.org/10.1155/2013/213536 |
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