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Image Recovery via Truncated Weighted Schatten-p Norm Regularization

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Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11068))

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Abstract

Low-rank prior knowledge has indicated great superiority in the field of image processing. However, how to solve the NP-hard problem containing rank norm is crucial to the recovery results. In this paper, truncated weighted schatten-p norm, which is employed to approximate the rank function by taking advantages of both weighted nuclear norm and truncated schatten-p norm, has been proposed toward better exploiting low-rank property in image CS recovery. At last, we have developed an efficient iterative scheme based on alternating direction method of multipliers to accurately solve the nonconvex optimization model. Experimental results demonstrate that our proposed algorithm is exceeding the existing state-of-the-art methods, both visually and quantitatively.

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Acknowledgments

The authors would like to express their gratitude to the anonymous referees as well as the Editor and Associate Editor for their valuable comments which lead to substantial improvements of the paper. This work was supported by High-level Talent Scientific Research Foundation of Jinling Institute of Technology (No. jit-b-201801), National Natural Science Foundation of China (No. 61772272), Doctor Initial Captional of Jinling Institute of Technology Nanjing (No. jit-b-201508), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) (No. 30916014107).

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Authors and Affiliations

  1. Jinling Institute of Technology, Nanjing, 211169, China

    Lei Feng & Jun Zhu

  2. Nanjing University of Science and Technology, Nanjing, 210094, China

    Jun Zhu

Authors
  1. Lei Feng
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  2. Jun Zhu
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Corresponding author

Correspondence to Lei Feng .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Feng, L., Zhu, J. (2018). Image Recovery via Truncated Weighted Schatten-p Norm Regularization. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_50

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  • DOI: https://doi.org/10.1007/978-3-030-00021-9_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00020-2

  • Online ISBN: 978-3-030-00021-9

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