Multiplicative noise removal with spatially varying regularization parameters

Fang Li, Michael K. Ng*, Chaomin Shen

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

62 Citations (Scopus)
32 Downloads (Pure)


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
Issue number1
Publication statusPublished - 22 Jan 2010

Scopus Subject Areas

  • Mathematics(all)
  • Applied Mathematics

User-Defined Keywords

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


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