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

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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.

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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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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, 100049, China

    Chun Li, Yuepeng Li, Zhicheng Zhao & Longlong Yu

  2. e-Science Technology and Application Laboratory, Computer Network Information Centre, Chinese Academy of Sciences, Beijing, 100190, China

    Chun Li, Zhicheng Zhao, Longlong Yu & Ze Luo

  3. Department of Big Data Technology and Application, Computer Network Information Centre, Chinese Academy of Sciences, Beijing, 100190, China

    Yuepeng Li

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  1. Chun Li
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Li, C., Li, Y., Zhao, Z. et al. A mixed noise removal algorithm based on multi-fidelity modeling with nonsmooth and nonconvex regularization. Multimed Tools Appl 78, 23117–23140 (2019). https://doi.org/10.1007/s11042-019-7625-1

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