A fast minimization method for blur and multiplicative noise removal

Fan Wang, Kwok Po NG*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

18 Citations (Scopus)


Multiplicative noise and blur removal problems have attracted much attention in recent years. In this paper, we propose an efficient minimization method to recover images from input blurred and multiplicative noisy images. In the proposed algorithm, we make use of the logarithm to transform blurring and multiplicative noise problems into additive image degradation problems, and then employ l 1-norm to measure in the data-fitting term and the total variation to measure the regularization term. The alternating direction method of multipliers (ADMM) is used to solve the corresponding minimization problem. In order to guarantee the convergence of the ADMM algorithm, we approximate the associated nonconvex domain of the minimization problem by a convex domain. Experimental results are given to demonstrate that the proposed algorithm performs better than the other existing methods in terms of speed and peak signal noise ratio.

Original languageEnglish
Pages (from-to)48-61
Number of pages14
JournalInternational Journal of Computer Mathematics
Issue number1
Publication statusPublished - 1 Jan 2013

Scopus Subject Areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Blur
  • image restoration
  • Iterative method
  • minimization
  • Multiplicative noise


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