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Image Restoration via Multi-prior Collaboration

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9005))

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Abstract

This paper proposes a novel multi-prior collaboration framework for image restoration. Different from traditional non-reference image restoration methods, a big reference image set is adopted to provide the references and predictions of different popular prior models and accordingly further guide the subsequent multi-prior collaboration. In particular, the collaboration of multi-prior models is mathematically formulated as a ridge regression problem. Due to expensive computation complexity of handling big reference data, scatter-matrix-based kernel ridge regression is proposed, which achieves high accuracy while low complexity. Additionally, an iterative pursuit is further proposed to obtain refined and robust restoration results. Five popular prior methods are applied to evaluate the effectiveness of the proposed multi-prior collaboration framework. Compared with the state-of-the-art image restoration approaches, the proposed framework improves the restoration performance significantly.

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Acknowledgement

This work was supported in part by the Major State Basic Research Development Program of China (973 Program 2015CB351804) and the National Natural Science Foundation of China under Grant No. 61272386, 61100096 and 61300111.

Author information

Authors and Affiliations

  1. School of Computer Science, Harbin Institute of Technology, Harbin, China

    Feng Jiang, Shengping Zhang & Debin Zhao

  2. School of Electrical Engineering, Princeton University, Princeton, USA

    Feng Jiang & S. Y. Kung

Authors
  1. Feng Jiang
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  2. Shengping Zhang
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  3. Debin Zhao
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  4. S. Y. Kung
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Corresponding author

Correspondence to Feng Jiang .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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© 2015 Springer International Publishing Switzerland

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Jiang, F., Zhang, S., Zhao, D., Kung, S.Y. (2015). Image Restoration via Multi-prior Collaboration. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-16811-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16810-4

  • Online ISBN: 978-3-319-16811-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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