Blind Image Deblurring via a Novel Sparse Channel Prior

Dayi Yang, Xiaojun Wu and Hefeng Yin
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Dayi Yang: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Xiaojun Wu: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
Hefeng Yin: School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China

Mathematics, 2022, vol. 10, issue 8, 1-17

Abstract: Blind image deblurring (BID) is a long-standing challenging problem in low-level image processing. To achieve visually pleasing results, it is of utmost importance to select good image priors. In this work, we develop the ratio of the dark channel prior (DCP) to the bright channel prior (BCP) as an image prior for solving the BID problem. Specifically, the above two channel priors obtained from RGB images are used to construct an innovative sparse channel prior at first, and then the learned prior is incorporated into the BID tasks. The proposed sparse channel prior enhances the sparsity of the DCP. At the same time, it also shows the inverse relationship between the DCP and BCP. We employ the auxiliary variable technique to integrate the proposed sparse prior information into the iterative restoration procedure. Extensive experiments on real and synthetic blurry sets show that the proposed algorithm is efficient and competitive compared with the state-of-the-art methods and that the proposed sparse channel prior for blind deblurring is effective.

Keywords: blind image deblurring; image prior; sparse channel; sparsity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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