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Blind Image Deblurring via a Novel Sparse Channel Prior

Author

Listed:
  • Dayi Yang

    (School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
    Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China)

  • Xiaojun Wu

    (School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
    Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China)

  • Hefeng Yin

    (School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
    Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China)

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.

Suggested Citation

  • Dayi Yang & Xiaojun Wu & Hefeng Yin, 2022. "Blind Image Deblurring via a Novel Sparse Channel Prior," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1238-:d:790154
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    Citations

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    Cited by:

    1. Shahid Saleem & Shahbaz Ahmad & Junseok Kim, 2023. "Total Fractional-Order Variation-Based Constraint Image Deblurring Problem," Mathematics, MDPI, vol. 11(13), pages 1-26, June.
    2. Weihua Ou & Jianping Gou & Shaoning Zeng & Lan Du, 2023. "Preface to the Special Issue “Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics”—Special Issue Book," Mathematics, MDPI, vol. 11(4), pages 1-4, February.

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