A Primal-Dual Hybrid Gradient Algorithm to Solve the LLT Model for Image Denoising

被引：1
|
作者：
Liu, Chunxiao [1 ]
Kong, Dexing [2 ]
Zhu, Shengfeng [2 ]
机构：
[1] Hangzhou Normal Univ, Dept Math, Hangzhou 310036, Zhejiang, Peoples R China
[2] Zhejiang Univ, Dept Math, Hangzhou 310003, Zhejiang, Peoples R China
来源：
NUMERICAL MATHEMATICS-THEORY METHODS AND APPLICATIONS | 2012年 / 5卷 / 02期
关键词：
LLT model; image denoising; primal-dual; TOTAL VARIATION MINIMIZATION; FILTER;
D O I：
10.4208/nmtma.2012.m1047
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We propose an efficient gradient-type algorithm to solve the fourth-order LLT denoising model for both gray-scale and vector-valued images. Based on the primal-dual formulation of the original nondifferentiable model, the new algorithm updates the primal and dual variables alternately using the gradient descent/ascent flows. Numerical examples are provided to demonstrate the superiority of our algorithm.
引用
收藏
页码：260 / 277
页数：18
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