Cauchy noise removal using group-based low-rank prior

Meng Ding, Ting-Zhu Huang, Tian-Hui Ma, Xi-Le Zhao and Jing-Hua Yang

Applied Mathematics and Computation, 2020, vol. 372, issue C

Abstract: Although the extensive research on Gaussian noise removal, few works consider the Cauchy noise removal problem. In this paper, we propose a novel group-based low-rank method for Cauchy noise removal. By exploiting the nonlocal self-similarity of natural images, we consider a group of similar patches as an approximate low-rank matrix, and formulate the denoising of each group as a low-rank matrix recovery problem. Meanwhile, we develop the alternating direction method of multipliers algorithm to solve the proposed nonconvex model with guaranteed convergence. Experiments illustrate that our method has superior performance over the state-of-the-art methods in terms of both visual and quantitative measures.

Keywords: Cauchy noise; Nonlocal self-similarity; Low-rank matrix recovery; Alternating direction method of multipliers (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:372:y:2020:i:c:s0096300319309634

DOI: 10.1016/j.amc.2019.124971

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