Enhanced Patch-Wise Maximal Gradient for Blind Image Deblurring.
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| Title: | Enhanced Patch-Wise Maximal Gradient for Blind Image Deblurring. |
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| Authors: | zhang, Zirui1 (AUTHOR) 321113010254@njust.edu.cn, Guo, Zheng1 (AUTHOR) guozheng1999@njust.edu.cn, Xu, Zhenhua2 (AUTHOR) xzh@njust.edu.cn, Wang, Chunyong1 (AUTHOR) wangcyong@njust.edu.cn, Lai, Jiancheng1 (AUTHOR) laijiancheng@njust.edu.cn, Ji, Yunjing1 (AUTHOR) jyunjing@njust.edu.cn, Li, Zhenhua1 (AUTHOR) lizhenhua@njust.edu.cn |
| Source: | Circuits, Systems & Signal Processing. Aug2025, Vol. 44 Issue 8, p6227-6254. 28p. |
| Subjects: | Distribution (Probability theory), Simplicity |
| Abstract: | The maximum intensity and gradient values of non-overlapping patches significantly decrease during the blurring process. To address this issue, we propose an enhanced patch-wise maximum gradient (EPMG) prior for blind image deblurring. We evaluate the statistical distribution of EPMG using a real dataset and mathematically demonstrate its effectiveness. Based on the EPMG prior, we develop an effective deblurring model incorporating an L0 regularized EPMG prior and an L0 regularized gradient prior. Unlike previous priors, our EPMG prior considers both intensity and gradient information, enabling a more comprehensive distinction between clear and blurred images. Additionally, the non-overlapping patch design we adopt ensures sparsity and simplicity, significantly enhancing computational efficiency. Extensive experimental results demonstrate that our method yields superior visual quality and quantitative performance compared to several state-of-the-art methods, particularly in computational efficiency and restoration quality. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | The maximum intensity and gradient values of non-overlapping patches significantly decrease during the blurring process. To address this issue, we propose an enhanced patch-wise maximum gradient (EPMG) prior for blind image deblurring. We evaluate the statistical distribution of EPMG using a real dataset and mathematically demonstrate its effectiveness. Based on the EPMG prior, we develop an effective deblurring model incorporating an L0 regularized EPMG prior and an L0 regularized gradient prior. Unlike previous priors, our EPMG prior considers both intensity and gradient information, enabling a more comprehensive distinction between clear and blurred images. Additionally, the non-overlapping patch design we adopt ensures sparsity and simplicity, significantly enhancing computational efficiency. Extensive experimental results demonstrate that our method yields superior visual quality and quantitative performance compared to several state-of-the-art methods, particularly in computational efficiency and restoration quality. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 0278081X |
| DOI: | 10.1007/s00034-025-03106-9 |
