On algorithms for automatic deblurring from a single image

Wei Wang*, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

In this paper, we study two variational blind deblurring models for a single image. The first model is to use the total variation prior in both image and blur, while the second model is to use the frame based prior in both image and blur. The main contribution of this paper is to show how to employ the generalized cross validation (GCV) method efficiently and automatically to estimate the two regularization parameters associated with the priors in these two blind motion deblurring models. Our experimental results show that the visual quality of restored images by the proposed method is very good, and they are competitive with the tested existing methods. We will also demonstrate the proposed method is also very efficient.

Original languageEnglish
Pages (from-to)80-100
Number of pages21
JournalJournal of Computational Mathematics
Volume30
Issue number1
DOIs
Publication statusPublished - Jan 2012

Scopus Subject Areas

  • Computational Mathematics

User-Defined Keywords

  • Blind deconvolution
  • Framelet
  • Generalized cross validation
  • Iterative methods
  • Total variation

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