A New Total Variation Method for Multiplicative Noise Removal

Yu Mei Huang, Michael K. Ng*, You Wei Wen

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

278 Citations (Scopus)
101 Downloads (Pure)


Multiplicative noise removal problems have attracted much attention in recent years. Unlike additive noise removal problems, the noise is multiplied to the orginal image, so almost all information of the original image may disappear in the observed image. The main aim of this paper is to propose and study a strictly convex objective function for multiplicative noise removal problems. We also incorporate the modified total variation regularization in the objective function to recover image edges. We develop an alternating minimization algorithm to find the minimizer of such an objective function efficiently and also show the convergence of the minimizing method. Our experimental results show that the quality of images denoised by the proposed method is quite good.

Original languageEnglish
Pages (from-to)20-40
Number of pages21
JournalSIAM Journal on Imaging Sciences
Issue number1
Publication statusPublished - 14 Jan 2009

Scopus Subject Areas

  • Mathematics(all)
  • Applied Mathematics

User-Defined Keywords

  • Convex function
  • Image denoising
  • Multiplicative noise
  • Total variation


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