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Pacific-Rim Symposium on Image and Video Technology

PSIVT 2017: Image and Video Technology pp 283–299Cite as

Blind Image Deblurring via Salient Structure Detection and Sparse Representation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10799))

Abstract

Blind image deblurring algorithms have been improving steadily in the past years. However, most state-of-the-art algorithms still cannot perform perfectly in challenging cases, e.g., when the blurred image contains complex tiny structures or the blur kernel is large. This paper presents a new algorithm that combines salient image structure detection and sparse representation for blind image deblurring. Salient structures provide reliable edge information from the blurred image, while sparse representation provides data-authentic priors for both the blur kernel and the latent image. When estimating the kernel, the salient structures are extracted from an interim latent image solved by combining the predicted structure and spatial and sparsity priors, which help preserve more sharp edges than previous deconvolution methods do. We also aim at removing noise and preserving continuity in the kernel, thus obtaining a high-quality blur kernel. Then a sparse representation based \(\ell _1\)-norm deconvolution model is proposed for suppressing noise robustly and solving for a high-quality latent image. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples.

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Notes

  1. 1.

    Image details is referred to as \(I - I_s\).

  2. 2.

    We replace \(I_s\) with \(I - I_s\) in (7), then result shown in (a) is obtained in the same way as (d).

  3. 3.

    In this paper, we regard noise and saturation pixels as outliers, and do not take any special strategy to deal with them. Thus, result of [33] performs better. However, our result is comparable with that of [33].

  4. 4.

    The Matlab codes are available on authors’ webpages.

  5. 5.

    We only present the deblurred results with the error ratio (smaller than 3). Otherwise, We find that the results will be unreliable.

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Authors and Affiliations

  1. School of Mathematical Sciences, Dalian University of Technology, Dalian, China

    Yu Cai, Jinshan Pan & Zhixun Su

Authors
  1. Yu Cai
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  2. Jinshan Pan
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  3. Zhixun Su
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Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Shin'ichi Satoh

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Cai, Y., Pan, J., Su, Z. (2018). Blind Image Deblurring via Salient Structure Detection and Sparse Representation. In: Satoh, S. (eds) Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science(), vol 10799. Springer, Cham. https://doi.org/10.1007/978-3-319-92753-4_23

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  • DOI: https://doi.org/10.1007/978-3-319-92753-4_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92752-7

  • Online ISBN: 978-3-319-92753-4

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