A cartoon-plus-texture image decomposition model for blind deconvolution

Wei Wang*, Xile Zhao, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

In this paper, we study a blind deconvolution problem by using an image decomposition technique. Our idea is to make use of a cartoon-plus-texture image decomposition procedure into the deconvolution problem. Because cartoon and texture components can be represented differently in images, we can adapt suitable regularization methods to restore their components. In particular, the total variational regularization is used to describe the cartoon component, and Meyer’s G-norm is employed to model the texture component. In order to obtain the restored image automatically, we also use the generalized cross validation method efficiently and effectively to estimate their corresponding regularization parameters. Experimental results are reported to demonstrate that the visual quality of restored images by using the proposed method is very good, and is competitive with the other testing methods.

Original languageEnglish
Pages (from-to)541-562
Number of pages22
JournalMultidimensional Systems and Signal Processing
Volume27
Issue number2
DOIs
Publication statusPublished - 1 Apr 2016

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • Alternating minimization
  • Blind deconvolution
  • Cartoon
  • Image decomposition
  • Regularization
  • Texture
  • Total variation

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