A convex relaxation method for computing exact global solutions for multiplicative noise removal

Chunxiao Liu; Shengfeng Zhu

doi:10.1016/j.cam.2012.08.019

A convex relaxation method for computing exact global solutions for multiplicative noise removal

Chunxiao Liu
, Shengfeng Zhu^*

^*此作品的通讯作者

数学科学学院

Hangzhou Normal University

科研成果: 期刊稿件 › 文章 › 同行评审

6 引用（Scopus）

摘要

We propose a convex relaxation technique for computing global solutions for the nonconvex multiplicative noise model. The method is based on functional lifting by introducing an additional dimension. We employ a primal-dual-based gradient-type algorithm in numerical implementations to overcome the nondifferentiability of the total variation term. Numerical results show that our algorithm is highly efficient. Furthermore, global solutions of the original model can be obtained with no dependence on the initial guess.

源语言	英语
页（从-至）	144-155
页数	12
期刊	Journal of Computational and Applied Mathematics
卷	238
期	1
DOI	https://doi.org/10.1016/j.cam.2012.08.019
出版状态	已出版 - 15 1月 2013

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N2 - We propose a convex relaxation technique for computing global solutions for the nonconvex multiplicative noise model. The method is based on functional lifting by introducing an additional dimension. We employ a primal-dual-based gradient-type algorithm in numerical implementations to overcome the nondifferentiability of the total variation term. Numerical results show that our algorithm is highly efficient. Furthermore, global solutions of the original model can be obtained with no dependence on the initial guess.

AB - We propose a convex relaxation technique for computing global solutions for the nonconvex multiplicative noise model. The method is based on functional lifting by introducing an additional dimension. We employ a primal-dual-based gradient-type algorithm in numerical implementations to overcome the nondifferentiability of the total variation term. Numerical results show that our algorithm is highly efficient. Furthermore, global solutions of the original model can be obtained with no dependence on the initial guess.

KW - Global minimization

KW - Image denoising

KW - Multiplicative noise

KW - Primal-dual

KW - Total variation

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A convex relaxation method for computing exact global solutions for multiplicative noise removal

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