Dual-Channel Contrast Prior for Blind Image Deblurring

In this article, a dual-channel contrast prior (Dual-CP) is proposed for blind image deblurring. The prior is motivated by the observation that image contrast will significantly degenerate after the blurring process, which is proved in both mathematically and empirically. Based on this inherent prop...

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Main Authors: Dayi Yang, Xiaojun Wu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9298847/
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author Dayi Yang
Xiaojun Wu
author_facet Dayi Yang
Xiaojun Wu
author_sort Dayi Yang
collection DOAJ
description In this article, a dual-channel contrast prior (Dual-CP) is proposed for blind image deblurring. The prior is motivated by the observation that image contrast will significantly degenerate after the blurring process, which is proved in both mathematically and empirically. Based on this inherent property of the blurring process, we analyze the variation of contrast influenced by blur and research the feasibility for using contrast prior to estimate blur kernel. We model the contrast by the difference between the dark channel and the bright channel. By maximizing the contrast in the local patch, we can obtain a reliable result which contains sharp edges and is beneficial for kernel estimation. To solve this non-convex nonlinear problem, we develop an efficient optimization method with the auxiliary variable idea and alternate direction minimization. Extensive experiments on real and synthetic blurry sets demonstrate that the proposed algorithm has good performance and exhibits competitiveness compared with state-of-the-art methods. Besides, we show that the proposed method can be applied to non-uniform deblurring.
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spelling doaj.art-971e461227e6401f97691198822e91db2022-12-22T03:47:03ZengIEEEIEEE Access2169-35362020-01-01822787922789310.1109/ACCESS.2020.30458579298847Dual-Channel Contrast Prior for Blind Image DeblurringDayi Yang0https://orcid.org/0000-0003-1747-1987Xiaojun Wu1School of Internet of Things Engineering, Jiangnan University, Wuxi, ChinaSchool of Internet of Things Engineering, Jiangnan University, Wuxi, ChinaIn this article, a dual-channel contrast prior (Dual-CP) is proposed for blind image deblurring. The prior is motivated by the observation that image contrast will significantly degenerate after the blurring process, which is proved in both mathematically and empirically. Based on this inherent property of the blurring process, we analyze the variation of contrast influenced by blur and research the feasibility for using contrast prior to estimate blur kernel. We model the contrast by the difference between the dark channel and the bright channel. By maximizing the contrast in the local patch, we can obtain a reliable result which contains sharp edges and is beneficial for kernel estimation. To solve this non-convex nonlinear problem, we develop an efficient optimization method with the auxiliary variable idea and alternate direction minimization. Extensive experiments on real and synthetic blurry sets demonstrate that the proposed algorithm has good performance and exhibits competitiveness compared with state-of-the-art methods. Besides, we show that the proposed method can be applied to non-uniform deblurring.https://ieeexplore.ieee.org/document/9298847/Blind image deblurringcontrastdual-channel contrast prior
spellingShingle Dayi Yang
Xiaojun Wu
Dual-Channel Contrast Prior for Blind Image Deblurring
IEEE Access
Blind image deblurring
contrast
dual-channel contrast prior
title Dual-Channel Contrast Prior for Blind Image Deblurring
title_full Dual-Channel Contrast Prior for Blind Image Deblurring
title_fullStr Dual-Channel Contrast Prior for Blind Image Deblurring
title_full_unstemmed Dual-Channel Contrast Prior for Blind Image Deblurring
title_short Dual-Channel Contrast Prior for Blind Image Deblurring
title_sort dual channel contrast prior for blind image deblurring
topic Blind image deblurring
contrast
dual-channel contrast prior
url https://ieeexplore.ieee.org/document/9298847/
work_keys_str_mv AT dayiyang dualchannelcontrastpriorforblindimagedeblurring
AT xiaojunwu dualchannelcontrastpriorforblindimagedeblurring