A convex variational model for restoring blurred images with multiplicative noise

Yiqiu Dong, Tieyong ZENG

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)

Abstract

In this paper, a new variational model for restoring blurred images with multiplicative noise is proposed. Based on the statistical property of the noise, a quadratic penalty function technique is utilized in order to obtain a strictly convex model under a mild condition, which guarantees the uniqueness of the solution and the stabilization of the algorithm. For solving the new convex variational model, a primal-dual algorithm is proposed, and its convergence is studied. The paper ends with a report on numerical tests for the simultaneous deblurring and denoising of images subject to multiplicative noise. A comparison with other methods is provided as well.

Original languageEnglish
Pages (from-to)1598-1625
Number of pages28
JournalSIAM Journal on Imaging Sciences
Volume6
Issue number3
DOIs
Publication statusPublished - 2013

Scopus Subject Areas

  • Mathematics(all)
  • Applied Mathematics

User-Defined Keywords

  • Convexity
  • Deblurring
  • Multiplicative noise
  • Primal-dual algorithm
  • Total variation regularization
  • Variational model

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