Fixed-point algorithms for a TVL1 image restoration model

Jian Lu, Ke Qiao, Lixin Shen, Yuru Zou

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

In this paper, we study fixed point proximity algorithms for a TVL1 restoration model recovering blurred images with impulsive noise, and image inpainting. The model that minimizes the sum of a data fidelity term in the (Formula presented.)-norm, a term in (Formula presented.)-norm and total-variation regularization term is strictly convex. We obtain the solution of the model through finding a fixed point of a nonlinear mapping expressed in terms of the proximity operator of the (Formula presented.)-norm or the (Formula presented.)-norm, each of which is explicitly given. The non-expansivity of the mapping is also analysed theoretically. This formulation naturally leads to fixed-point algorithms for numerical treatment of the model. Numerical experiments demonstrate that the proposed algorithms perform favourably.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalInternational Journal of Computer Mathematics
DOIs
StateAccepted/In press - Jul 1 2017

Keywords

  • deblurring
  • impulsive noise
  • inpainting
  • proximity operator
  • Total variation

ASJC Scopus subject areas

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

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