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The Convex Relaxation Method on Deconvolution Model withMultiplicative Noise

Published online by Cambridge University Press:  03 June 2015

Yumei Huang ,
Michael Ng  and
Tieyong Zeng
Yumei Huang
Affiliation:
School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
Michael Ng
Affiliation:
Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Tieyong Zeng*
Affiliation:
Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
*
*Corresponding author.Email:zeng@hkbu.edu.hk
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Abstract

In this paper, we consider variational approaches to handle the multiplicative noise removal and deblurring problem. Based on rather reasonable physical blurring-noisy assumptions, we derive a new variational model for this issue. After the study of the basic properties, we propose to approximate it by a convex relaxation model which is a balance between the previous non-convex model and a convex model. The relaxed model is solved by an alternating minimization approach. Numerical examples are presented to illustrate the effectiveness and efficiency of the proposed method.

Keywords

52A4065K1065K1590C26Alternating minimizationconvergencedeblurringmultiplicative noisenon-convex model

Information

Type
Research Article
Information
Communications in Computational Physics , Volume 13 , Issue 4 , April 2013 , pp. 1066 - 1092
Copyright
Copyright © Global Science Press Limited 2013

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