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Multimedia Tools and Applications

, Volume 78, Issue 16, pp 23117–23140 | Cite as

A mixed noise removal algorithm based on multi-fidelity modeling with nonsmooth and nonconvex regularization

  • Chun LiEmail author
  • Yuepeng Li
  • Zhicheng Zhao
  • Longlong Yu
  • Ze Luo
Article
  • 168 Downloads

Abstract

In this article, we propose a mixed-noise removal model which incorporates with a nonsmooth and nonconvex regularizer. To solve this model, a multistage convex relaxation method is used to deal with the optimization problem due to the nonconvex regularizer. Besides, we adopt the number of iteration steps as the termination condition of the proposed algorithm and select the optimal parameters for the model by a genetic algorithm. Several experiments on classic images with different level noises indicate that the robustness, running time, ISNR (Improvement in Signalto-Noise ratio) and PSNR (Peak Signal to Noise Ratio) of our model are better than those of other three models, and the proposed model can retain the local information of the image to obtain the optimal quantitative metrics and visual quality of the restored images.

Keywords

Image restoration Inverse problem Alternating direction method of multipliers Nonconvex optimization 

Notes

Acknowledgments

Funding were provided by the Natural Science Foundation of China under Grant NO.61361126011, No. 90912006; the Special Project of Informatization of Chinese Academy of Sciences in “the Twelfth Five-Year Plan” under Grant No. XXH12504-1-06, Science and Technology Service Network Initiative, CAS, (STS Plan); he IT integrated service platform of Sichuan Wolong Natural Reserve, under Grant No. Y82E01; The National R&D Infrastructure and Facility Development Program of China, “Fundamental Science Data Sharing Platform” (DKA2018-12-02-XX); Supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA19060205; the Special Project of Informatization of Chinese Academy of Sciences (XXH13505-03-205); the Special Project of Informatization of Chinese Academy of Sciences (XXH13506-305); the Special Project of Informatization of Chinese Academy of Sciences (XXH13506-303); Supported by Around Five Top Priorities of “One-Three-Five” Strategic Planning, CNIC(Grant No. CNIC_PY-1408); Supported by Around Five Top Priorities of “One-Three-Five” Strategic Planning, CNIC(Grant No. CNIC_PY-1409) The authors wish to gratefully thank all anoymous reriewers who provided insightful and helpful comments.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Chun Li
    • 1
    • 2
    Email author
  • Yuepeng Li
    • 1
    • 3
  • Zhicheng Zhao
    • 1
    • 2
  • Longlong Yu
    • 1
    • 2
  • Ze Luo
    • 2
  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.e-Science Technology and Application Laboratory, Computer Network Information CentreChinese Academy of SciencesBeijingChina
  3. 3.Department of Big Data Technology and Application, Computer Network Information CentreChinese Academy of SciencesBeijingChina

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