Multiplicative noise removal with spatially varying regularization parameters

Fang Li, Kwok Po NG, Chaomin Shen

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)

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 languageEnglish
Pages (from-to)1-20
Number of pages20
JournalSIAM Journal on Imaging Sciences
Volume3
Issue number1
DOIs
Publication statusPublished - 2010

Scopus Subject Areas

  • Mathematics(all)
  • Applied Mathematics

User-Defined Keywords

  • Multiplicative noise
  • Spatially varying regularization parameters
  • Textures
  • Total variation

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