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Edge-Based Blur Kernel Estimation Using Sparse Representation and Self-similarity

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12889))

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Abstract

Blind image deconvolution is the problem of recovering the latent image from the only observed blurry image when the blur kernel is unknown. In this paper, we propose an edge-based blur kernel estimation method for blind motion deconvolution. In our previous work, we incorporate both sparse representation and self-similarity of image patches as priors into our blind deconvolution model to regularize the recovery of the latent image. Since almost any natural image has properties of sparsity and multi-scale self-similarity, we construct a sparsity regularizer and a cross-scale non-local regularizer based on our patch priors. It has been observed that our regularizers often favor sharp images over blurry ones only for image patches of the salient edges and thus we define an edge mask to locate salient edges that we want to apply our regularizers. Experimental results on both simulated and real blurry images demonstrate that our method outperforms existing state-of-the-art blind deblurring methods even for handling of very large blurs, thanks to the use of the edge mask.

Scientific Research Common Program of Beijing Municipal Commission of Education (KM201910005029) and Beijing Natural Science Foundation (4212014).

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Author information

Authors and Affiliations

  1. Beijing University of Technology, Beijing, 100124, China

    Jing Yu, Lening Guo & Chuangbai Xiao

  2. Tsinghua University, Beijing, 100084, China

    Zhenchun Chang

Authors
  1. Jing Yu
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  2. Lening Guo
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  3. Chuangbai Xiao
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  4. Zhenchun Chang
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Corresponding author

Correspondence to Chuangbai Xiao .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

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Yu, J., Guo, L., Xiao, C., Chang, Z. (2021). Edge-Based Blur Kernel Estimation Using Sparse Representation and Self-similarity. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-87358-5_15

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