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Sparsity-Promoting Adaptive Coding with Robust Empirical Mode Decomposition for Image Restoration

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Advances in Multimedia Information Processing – PCM 2017 (PCM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10735))

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Abstract

In this paper, a novel data-driven sparse coding framework is proposed to solve image restoration problem based on a robust empirical mode decomposition. This powerful analysis tool for multi-dimensional signals can adaptively decompose images into multiscale oscillating components according to intrinsic modes of data self. This treatment can obtain very effective sparse representation, and meanwhile generates a dictionary at multiple geometric scales and frequency bands. The distribution of sparse coefficients is reliably approximated by generalized Gaussian model. Moreover, a sparse approximation of blur kernel is also obtained as a strong prior. Finally, latent image and blur kernel can be jointly estimated via alternating optimization scheme. The extensive experiments show that our approach can effectively and efficiently recover the sharpness of local structures and suppress undesirable artifacts.

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Authors and Affiliations

  1. National Engineering Laboratory for Video Technology, Peking University, Beijing, China

    Rui Chen, Huizhu Jia, Xiaodong Xie & Gao Wen

Authors
  1. Rui Chen
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  2. Huizhu Jia
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  3. Xiaodong Xie
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  4. Gao Wen
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Corresponding author

Correspondence to Rui Chen .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Bing Zeng

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

  3. University of Ottawa, Ottawa, Ontario, Canada

    Abdulmotaleb El Saddik

  4. University of Electronic Science and Technology of China, Chengdu, China

    Hongliang Li

  5. Chinese Academy of Sciences, Beijing, China

    Shuqiang Jiang

  6. Harbin Institute of Technology, Harbin, China

    Xiaopeng Fan

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Chen, R., Jia, H., Xie, X., Wen, G. (2018). Sparsity-Promoting Adaptive Coding with Robust Empirical Mode Decomposition for Image Restoration. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_36

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  • DOI: https://doi.org/10.1007/978-3-319-77380-3_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77379-7

  • Online ISBN: 978-3-319-77380-3

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