Blind image deblurring based on dictionary replacing

Haisen Li, Yanning Zhang, Feng Duan, Yu Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Traditional image deblurring is based on deconvolution, an ill-posed problem, which is sensitive to the accuracy of the blur kernel. In this paper, we propose a blind image deblurring method based on dictionary replacing. First, we estimate the blur kernel from the blur image , and then based on the sparse representation of the image patch under over-complete dictionary, we deblur the image via replacing blur dictionary with clear dictionary. Our method avoids the deconvolution problem and can bring more high-frequency information in the deblurred image via dictionary replacing. Experimental results compared with state-of-the-art blind deblurring methods demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationIntelligent Science and Intelligent Data Engineering - Second Sino-Foreign-Interchange Workshop, IScIDE 2011, Revised Selected Papers
Pages357-364
Number of pages8
DOIs
StatePublished - 2012
Event2nd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2011 - Xi'an, China
Duration: 23 Oct 201125 Oct 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7202 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2011
Country/TerritoryChina
CityXi'an
Period23/10/1125/10/11

Keywords

  • blind image deblurring
  • dictionary learnings
  • dictionary replacing
  • sparse representation

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