Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

A new convex optimization model for multiplicative noise and blur removal

Language

English

Abstract

The main contribution of this paper is to propose a new convex optimization model for multiplicative noise and blur removal. The main idea is to rewrite a blur and multiplicative noise equation such that both the image variable and the noise variable are decoupled. The resulting objective function involves the total variation regularization term, the term of variance of the inverse of noise, the l1-norm of the data-fitting term among the observed image, and noise and image variables. Such a convex minimization model can be solved efficiently by using many numerical methods in the literature. Numerical examples are presented to demonstrate the effectiveness of the proposed model. Experimental results show that the proposed model can handle blur and multiplicative noise (Gamma, Gaussian, or Rayleigh distribution) removal quite well. © 2014 Society for Industrial and Applied Mathematics.

Keywords

Alternating direction method, Convex optimization, Image restoration, Multiplicative noise, Total variation

Publication Date

2014

Source Publication Title

SIAM Journal on Imaging Sciences

Volume

7

Issue

1

Start Page

456

End Page

475

Publisher

Society for Industrial and Applied Mathematics

DOI

10.1137/13092472X

Link to Publisher's Edition

http://dx.doi.org/10.1137/13092472X

ISSN (electronic)

19364954

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