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Low-rank constraint with sparse representation for image restoration under multiplicative noise

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Abstract

In this paper, a novel model is proposed to remove multiplicative noise via sparse representation and low-rank constraint. We first translate multiplicative noise into additive one by a logarithmic transform and introduce a regularization with nonlocal similarity and low-rank constraint to capture the essential features. To solve the proposed model, it is divided into three subproblems, and the alternative optimization method is employed. After the denoising image in the log-domain was available, the recovered image is obtained by an exponential function and a bias correction. Compared with the state-of-the-art methods, the proposed algorithm achieves better denoising results both in terms of objective metrics and visual effects.

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Notes

  1. http://www.imageprocessingplace.com/root files V3/image databases.

  2. http://sipi.usc.edu/services/database/index.html.

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Acknowledgements

This project is partially supported by the National Natural Science Foundation of China (61362021, 61661017, 61662014), Guangxi Natural Science Foundation (2016GXNSFAA-380043, 2013GXNSFDA019030), Guangxi Higher Education Undergraduate Teaching Reform Project (2017JGB230) and Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation (LDAC201704).

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Authors and Affiliations

  1. School of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, China

    Lixia Chen & Pingfang Zhu

  2. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China

    Xuewen Wang

Authors
  1. Lixia Chen
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  2. Pingfang Zhu
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  3. Xuewen Wang
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Correspondence to Xuewen Wang.

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Chen, L., Zhu, P. & Wang, X. Low-rank constraint with sparse representation for image restoration under multiplicative noise . SIViP 13, 179–187 (2019). https://doi.org/10.1007/s11760-018-1344-3

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