An effective document image deblurring algorithm

Xiaogang Chen, Xiangjian He, Jie Yang, Qiang Wu

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

58 Citations (Scopus)

Abstract

Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Traditional deblurring algorithms have been proposed to work for natural-scene images. However the natural-scene images are not consistent with document images. In this paper, the distinct characteristics of document images are investigated. We propose a content-aware prior for document image deblurring. It is based on document image foreground segmentation. Besides, an upper-bound constraint combined with total variation based method is proposed to suppress the rings in the deblurred image. Comparing with the traditional general purpose deblurring methods, the proposed deblurring algorithm can produce more pleasing results on document images. Encouraging experimental results demonstrate the efficacy of the proposed method.

Original languageEnglish
Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PublisherIEEE Computer Society
Pages369-376
Number of pages8
ISBN (Print)9781457703942
DOIs
Publication statusPublished - 2011
Externally publishedYes

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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