A Convex Variational Model for Restoring Blurred Images with Multiplicative Noise

Yiqiu Dong, Tieyong Zeng

Research output: Contribution to journalJournal articleResearchpeer-review

439 Downloads (Pure)

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
JournalS I A M Journal on Imaging Sciences
Volume6
Issue number3
Pages (from-to)1598-1625
ISSN1936-4954
DOIs
Publication statusPublished - 2013

Bibliographical note

© 2013 SIAM.

Keywords

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

Cite this