The convex relaxation method on deconvolution model with multiplicative noise

Yumei Huang, Kwok Po NG, Tieyong ZENG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

In this paper, we consider variational approaches to handle the multiplicative noise removal and deblurring problem. Based on rather reasonable physical blurring-noisy assumptions, we derive a new variational model for this issue. After the study of the basic properties, we propose to approximate it by a convex relaxation model which is a balance between the previous non-convex model and a convex model. The relaxed model is solved by an alternating minimization approach. Numerical examples are presentedto illustrate the effectivenessand efficiencyof the proposed method.

Original languageEnglish
Pages (from-to)1066-1092
Number of pages27
JournalCommunications in Computational Physics
Volume13
Issue number4
DOIs
Publication statusPublished - Apr 2013

Scopus Subject Areas

  • Physics and Astronomy (miscellaneous)

User-Defined Keywords

  • Alternating minimization
  • Convergence
  • Deblurring
  • Multiplicative noise
  • Non-convex model

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