ADAPTIVELY WEIGHTED DIFFERENCE MODEL OF ANISOTROPIC AND ISOTROPIC TOTAL VARIATION FOR IMAGE DENOISING

被引：2

作者：

Shi, Baoli ^{[1
]}

Li, Mengxia ^{[2
]}

Lou, Yifei ^{[3
]}

机构：

[1] Henan Univ, Dept Math, Kaifeng 475004, Peoples R China

[2] Anyang Univ, Sch Sci, Anyang 455000, Peoples R China

[3] Univ Texas Dallas, Dept Math Sci, Richardson, TX 75080 USA

来源：

JOURNAL OF NONLINEAR AND VARIATIONAL ANALYSIS | 2023年 / 7卷 / 04期

关键词：

Anisotropic and isotropic total variation model; Difference of convex function; Image denoising; Noconvex optimization; Primal dual method; REGULARIZATION; ALGORITHM; REPRESENTATION;

D O I：

10.23952/jnva.7.2023.4.07

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper proposes a novel nonconvex regularization functional by using an adaptively weighted difference model of anisotropic and isotropic total variation. By choosing the weights adap-tively at each pixel, our model can enhance the anisotropic diffusion so as to achieve robust image recovery. Regarding to numerical implementations, we express the proposed model into a saddle point problem with the help of a dual formulation of the total variation, followed by a primal dual method to find a model solution. Numerical experiments demonstrate that the proposed approach is superior over several gradient-based methods for image denoising in terms of both visual appearance and quantitative metrics of signal noise ratio (SNR) and structural similarity index measure (SSIM).

引用

页码：563 / 580

页数：18