Group-based weighted nuclear norm minimization for Cauchy noise removal with TV regularization

被引：0

作者：

Gao, Wen ^{[1
]}

Zhu, Jianguang ^{[1
]}

Hao, Binbin ^{[2
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China

[2] China Univ Petr, Coll Sci, Qingdao 266580, Peoples R China

来源：

DIGITAL SIGNAL PROCESSING | 2025年 / 156卷

基金：

中国国家自然科学基金;

关键词：

Image denoising; Cauchy noise; Alternating direction method of multipliers; Total variation; Weighted nuclear norm minimization; RESTORING BLURRED IMAGES; ALGORITHM; CNN;

D O I：

10.1016/j.dsp.2024.104836

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Cauchy noise, as a kind of impulsive and non-Gaussian noise, has recently received a lot of attention in the image processing. In this paper, we combine group-based low rank regularization and total variation (TV) regularization to propose a new hybrid variational model for Cauchy noise removal. In order to solve the proposed model, we develop an efficient alternating minimization method by incorporating the Chambolle projection algorithm, the weighted nuclear norm minimization algorithm, and Newton method. Numerical experiments demonstrate that the proposed method is superior to the existing state-of-the-art methods in terms of visual quality and quantitative measures.

引用

页数：13