Multiplicative noise removal with spatially varying regularization parameters

Fang Li; Michael K. Ng; Chaomin Shen

doi:10.1137/090748421

Multiplicative noise removal with spatially varying regularization parameters

Fang Li, Michael K. Ng^*, Chaomin Shen

^*Corresponding author for this work

Department of Mathematics

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

63 Citations (Scopus)

53 Downloads (Pure)

Abstract

The Aubert-Aujol (AA) model is a variational method for multiplicative noise removal. In this paper, we study some basic properties of the regularization parameter in the AA model. We develop a method for automatically choosing the regularization parameter in the multiplicative noise removal process. In particular, we employ spatially varying regularization parameters in the AA model in order to restore more texture details of the denoised image. Experimental results are presented to demonstrate that the spatially varying regularization parameters method can obtain better denoised images than the other tested multiplicative noise removal methods.

Original language	English
Pages (from-to)	1-20
Number of pages	20
Journal	SIAM Journal on Imaging Sciences
Volume	3
Issue number	1
DOIs	https://doi.org/10.1137/090748421
Publication status	Published - 22 Jan 2010

Scopus Subject Areas

General Mathematics
Applied Mathematics

User-Defined Keywords

Multiplicative noise
Spatially varying regularization parameters
Textures
Total variation

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abstract = "The Aubert-Aujol (AA) model is a variational method for multiplicative noise removal. In this paper, we study some basic properties of the regularization parameter in the AA model. We develop a method for automatically choosing the regularization parameter in the multiplicative noise removal process. In particular, we employ spatially varying regularization parameters in the AA model in order to restore more texture details of the denoised image. Experimental results are presented to demonstrate that the spatially varying regularization parameters method can obtain better denoised images than the other tested multiplicative noise removal methods.",

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AU - Shen, Chaomin

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N2 - The Aubert-Aujol (AA) model is a variational method for multiplicative noise removal. In this paper, we study some basic properties of the regularization parameter in the AA model. We develop a method for automatically choosing the regularization parameter in the multiplicative noise removal process. In particular, we employ spatially varying regularization parameters in the AA model in order to restore more texture details of the denoised image. Experimental results are presented to demonstrate that the spatially varying regularization parameters method can obtain better denoised images than the other tested multiplicative noise removal methods.

AB - The Aubert-Aujol (AA) model is a variational method for multiplicative noise removal. In this paper, we study some basic properties of the regularization parameter in the AA model. We develop a method for automatically choosing the regularization parameter in the multiplicative noise removal process. In particular, we employ spatially varying regularization parameters in the AA model in order to restore more texture details of the denoised image. Experimental results are presented to demonstrate that the spatially varying regularization parameters method can obtain better denoised images than the other tested multiplicative noise removal methods.

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KW - Total variation

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Multiplicative noise removal with spatially varying regularization parameters

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