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Structural smoothness low-rank matrix recovery via outlier estimation for image denoising

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Abstract

Natural images often have intrinsic low-rank structures and are susceptible to interference from outliers or perturbation noise, especially mixed noise. Low-rank matrix recovery via outlier estimation (ROUTE) has been proposed to determine the location of gross corruption by estimating the outliers; however, this approach ignores local structural smoothness. In this paper, we incorporate TV norm regularization into the ROUTE model of low-rank matrix recovery, which is called SSROUTE. This model can ensure structural smoothness in image denoising that is vulnerable to outlier noise and additive white Gaussian noise simultaneously. In addition, to solve the reformulated optimal problem, we develop an algorithm based on the alternating direction method of multipliers. Experimental results show that the proposed algorithm achieves a competitive denoising performance, especially for mixed noise.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (nos. 62072024, 61971290), the Research Ability Enhancement Program for Young Teachers of Beijing University of Civil Engineering and Architecture (no. X21024), the Talent Program of Beijing University of Civil Engineering and Architecture, the BUCEA Post Graduate Innovation Project, the Projects of Beijing Advanced Innovation Center for Future Urban Design (no. UDC2019033324), and the Fundamental Research Funds for Municipal Universities of Beijing University of Civil Engineering and Architecture (no. X20084).

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Authors and Affiliations

  1. School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Hengyou Wang, Wen Li, Changlun Zhang & Qiang He

  2. Beijing Key Laboratory of Functional Materials for Building Structure and Environment Remediation, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Hengyou Wang, Wen Li & Qiang He

  3. Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Lujin Hu & Changlun Zhang

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  1. Hengyou Wang
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  2. Wen Li
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  3. Lujin Hu
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Correspondence to Hengyou Wang.

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Communicated by C. Yan.

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Wang, H., Li, W., Hu, L. et al. Structural smoothness low-rank matrix recovery via outlier estimation for image denoising. Multimedia Systems 28, 241–255 (2022). https://doi.org/10.1007/s00530-021-00812-7

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