A primal-dual hybrid gradient algorithm to solve the LLT model for image denoising

Chunxiao Liu; Dexing Kong; Shengfeng Zhu

doi:10.4208/nmtma.2012.m1047

A primal-dual hybrid gradient algorithm to solve the LLT model for image denoising

Chunxiao Liu^*
, Dexing Kong
, Shengfeng Zhu

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	260-277
Number of pages	18
Journal	Numerical Mathematics
Volume	5
Issue number	2
DOIs	https://doi.org/10.4208/nmtma.2012.m1047
State	Published - May 2012
Externally published	Yes

Keywords

Image denoising
LLT model
Primal-dual

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10.4208/nmtma.2012.m1047

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title = "A primal-dual hybrid gradient algorithm to solve the LLT model for image denoising",

abstract = "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.",

keywords = "Image denoising, LLT model, Primal-dual",

author = "Chunxiao Liu and Dexing Kong and Shengfeng Zhu",

year = "2012",

month = may,

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AU - Liu, Chunxiao

AU - Kong, Dexing

AU - Zhu, Shengfeng

PY - 2012/5

Y1 - 2012/5

N2 - 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.

AB - 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.

KW - Image denoising

KW - LLT model

KW - Primal-dual

UR - https://www.scopus.com/pages/publications/84859964895

U2 - 10.4208/nmtma.2012.m1047

DO - 10.4208/nmtma.2012.m1047

M3 - 文献综述

AN - SCOPUS:84859964895

SN - 1004-8979

VL - 5

SP - 260

EP - 277

JO - Numerical Mathematics

JF - Numerical Mathematics

IS - 2

ER -

A primal-dual hybrid gradient algorithm to solve the LLT model for image denoising

Abstract

Keywords

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