Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix
To improve the efficiency of blur kernel estimation based on prior knowledge, a method of deblurring an image based on rich edge region extraction using a gray-level co-occurrence matrix is proposed in this paper. First, the relationship between the image edge information and the related coefficient...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8315437/ |
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| author | Minghua Zhao Xin Zhang Zhenghao Shi Peng Li Bing Li |
| author_facet | Minghua Zhao Xin Zhang Zhenghao Shi Peng Li Bing Li |
| author_sort | Minghua Zhao |
| collection | DOAJ |
| description | To improve the efficiency of blur kernel estimation based on prior knowledge, a method of deblurring an image based on rich edge region extraction using a gray-level co-occurrence matrix is proposed in this paper. First, the relationship between the image edge information and the related coefficients of a gray-level co-occurrence matrix is analyzed, based on which an index representing the amount of image edge information is proposed. Next, high-frequency layer information is extracted from the blurred image to be processed with a bilinear interpolation method in the luminance channel. Subsequently, the high-frequency layer image is divided into nine regions, based on a sliding window, and the rich edge region index of each region is calculated; then, the region with the richest edge information is extracted. Finally, the extracted rich edge region, instead of the entire motion blurred image, is used to estimate the blur kernel with L0-regularized intensity and gradient prior, and the blurred image is blindly restored. An image quality evaluation function and the operation time are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can improve the recovery efficiency while ensuring the recovery quality as well. |
| format | Article |
| id | doaj-art-2fd6f02d7b0c429f8ba947b104c73a39 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2fd6f02d7b0c429f8ba947b104c73a392025-08-20T02:25:04ZengIEEEIEEE Access2169-35362018-01-016155321554010.1109/ACCESS.2018.28156088315437Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence MatrixMinghua Zhao0https://orcid.org/0000-0001-8062-2982Xin Zhang1Zhenghao Shi2Peng Li3Bing Li4School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an, ChinaTo improve the efficiency of blur kernel estimation based on prior knowledge, a method of deblurring an image based on rich edge region extraction using a gray-level co-occurrence matrix is proposed in this paper. First, the relationship between the image edge information and the related coefficients of a gray-level co-occurrence matrix is analyzed, based on which an index representing the amount of image edge information is proposed. Next, high-frequency layer information is extracted from the blurred image to be processed with a bilinear interpolation method in the luminance channel. Subsequently, the high-frequency layer image is divided into nine regions, based on a sliding window, and the rich edge region index of each region is calculated; then, the region with the richest edge information is extracted. Finally, the extracted rich edge region, instead of the entire motion blurred image, is used to estimate the blur kernel with L0-regularized intensity and gradient prior, and the blurred image is blindly restored. An image quality evaluation function and the operation time are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can improve the recovery efficiency while ensuring the recovery quality as well.https://ieeexplore.ieee.org/document/8315437/Image processingimage restorationimage qualitymotion blurred imagegray-level co-occurrence matrixrich edge region |
| spellingShingle | Minghua Zhao Xin Zhang Zhenghao Shi Peng Li Bing Li Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix IEEE Access Image processing image restoration image quality motion blurred image gray-level co-occurrence matrix rich edge region |
| title | Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix |
| title_full | Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix |
| title_fullStr | Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix |
| title_full_unstemmed | Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix |
| title_short | Restoration of Motion Blurred Images Based on Rich Edge Region Extraction Using a Gray-Level Co-Occurrence Matrix |
| title_sort | restoration of motion blurred images based on rich edge region extraction using a gray level co occurrence matrix |
| topic | Image processing image restoration image quality motion blurred image gray-level co-occurrence matrix rich edge region |
| url | https://ieeexplore.ieee.org/document/8315437/ |
| work_keys_str_mv | AT minghuazhao restorationofmotionblurredimagesbasedonrichedgeregionextractionusingagraylevelcooccurrencematrix AT xinzhang restorationofmotionblurredimagesbasedonrichedgeregionextractionusingagraylevelcooccurrencematrix AT zhenghaoshi restorationofmotionblurredimagesbasedonrichedgeregionextractionusingagraylevelcooccurrencematrix AT pengli restorationofmotionblurredimagesbasedonrichedgeregionextractionusingagraylevelcooccurrencematrix AT bingli restorationofmotionblurredimagesbasedonrichedgeregionextractionusingagraylevelcooccurrencematrix |