Good regions to deblur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

64 Citations (Scopus)

Abstract

The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image. Recent works focus on exploiting natural image priors or additional image observations for deblurring, but pay less attention to the influence of image structures on estimating blur kernels. What is the useful image structure and how can one select good regions for deblurring? We formulate the problem of learning good regions for deblurring within the Conditional Random Field framework. To better compare blur kernels, we develop an effective similarity metric for labeling training samples. The learned model is able to predict good regions from an input blurred image for deblurring without user guidance. Qualitative and quantitative evaluations demonstrate that good regions can be selected by the proposed algorithms for effective image deblurring.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Pages59-72
Number of pages14
EditionPART 5
DOIs
Publication statusPublished - 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: 2012 Oct 72012 Oct 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 5
Volume7576 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th European Conference on Computer Vision, ECCV 2012
CountryItaly
CityFlorence
Period12/10/712/10/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Hu, Z., & Yang, M. H. (2012). Good regions to deblur. In Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings (PART 5 ed., pp. 59-72). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7576 LNCS, No. PART 5). https://doi.org/10.1007/978-3-642-33715-4_5