Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior
In this paper, an algorithm based on local binary pattern (LBP) is proposed to obtain clear remote sensing images under the premise of unknown causes of blurring. We find that LBP can completely record the texture features of the images, which will not change widely due to the generation of blur. Th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/5/1276 |
| _version_ | 1797473859124854784 |
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| author | Ziyu Zhang Liangliang Zheng Yongjie Piao Shuping Tao Wei Xu Tan Gao Xiaobin Wu |
| author_facet | Ziyu Zhang Liangliang Zheng Yongjie Piao Shuping Tao Wei Xu Tan Gao Xiaobin Wu |
| author_sort | Ziyu Zhang |
| collection | DOAJ |
| description | In this paper, an algorithm based on local binary pattern (LBP) is proposed to obtain clear remote sensing images under the premise of unknown causes of blurring. We find that LBP can completely record the texture features of the images, which will not change widely due to the generation of blur. Therefore, LBP prior is proposed, which can filter out the pixels containing important textures in the blurry image through the mapping relationship. The corresponding processing methods are adopted for different types of pixels to cope with the challenges brought by the rich texture and details of remote sensing images and prevent over-sharpening. However, the existence of LBP prior increases the difficulty of solving the model. To solve the model, we construct the projected alternating minimization (PAM) algorithm that involves the construction of the mapping matrix, the fast iterative shrinkage-thresholding algorithm (FISTA) and the half-quadratic splitting method. Experiments with the AID dataset show that the proposed method can achieve highly competitive processing results for remote sensing images. |
| first_indexed | 2024-03-09T20:22:37Z |
| format | Article |
| id | doaj.art-b6badf5afbf3464f9a3d2a7a20aa0402 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T20:22:37Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b6badf5afbf3464f9a3d2a7a20aa04022023-11-23T23:44:10ZengMDPI AGRemote Sensing2072-42922022-03-01145127610.3390/rs14051276Blind Remote Sensing Image Deblurring Using Local Binary Pattern PriorZiyu Zhang0Liangliang Zheng1Yongjie Piao2Shuping Tao3Wei Xu4Tan Gao5Xiaobin Wu6Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaIn this paper, an algorithm based on local binary pattern (LBP) is proposed to obtain clear remote sensing images under the premise of unknown causes of blurring. We find that LBP can completely record the texture features of the images, which will not change widely due to the generation of blur. Therefore, LBP prior is proposed, which can filter out the pixels containing important textures in the blurry image through the mapping relationship. The corresponding processing methods are adopted for different types of pixels to cope with the challenges brought by the rich texture and details of remote sensing images and prevent over-sharpening. However, the existence of LBP prior increases the difficulty of solving the model. To solve the model, we construct the projected alternating minimization (PAM) algorithm that involves the construction of the mapping matrix, the fast iterative shrinkage-thresholding algorithm (FISTA) and the half-quadratic splitting method. Experiments with the AID dataset show that the proposed method can achieve highly competitive processing results for remote sensing images.https://www.mdpi.com/2072-4292/14/5/1276blind image deblurringimage restorationLBP priorremote sensing image |
| spellingShingle | Ziyu Zhang Liangliang Zheng Yongjie Piao Shuping Tao Wei Xu Tan Gao Xiaobin Wu Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior Remote Sensing blind image deblurring image restoration LBP prior remote sensing image |
| title | Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior |
| title_full | Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior |
| title_fullStr | Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior |
| title_full_unstemmed | Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior |
| title_short | Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior |
| title_sort | blind remote sensing image deblurring using local binary pattern prior |
| topic | blind image deblurring image restoration LBP prior remote sensing image |
| url | https://www.mdpi.com/2072-4292/14/5/1276 |
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