Super resolution of text image by pruning outlier

Ziye Yan*, Yao Lu, Jianwu Li

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

We propose a learning based super resolution algorithm for single frame text image. The distance based candidate of example can't avoid the outliers and the super resolution result will be disturbed by the irrelevant outliers. In this work, the unique constraints of the text image are used to reject the outliers in the learning based SR algorithm. The final image is obtained by the Markov random field network with k nearest neighbor candidates from an image database that contains pairs of corresponding low resolution and high resolution text image patches. We demonstrate our algorithm on simulated and real scanned documents with promising results.

Original languageEnglish
Title of host publicationNeural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Pages649-656
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 2011
Event18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China
Duration: 13 Nov 201117 Nov 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7064 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Neural Information Processing, ICONIP 2011
Country/TerritoryChina
CityShanghai
Period13/11/1117/11/11

Keywords

  • Markov random field
  • Outlier
  • Super resolution
  • Text image

Fingerprint

Dive into the research topics of 'Super resolution of text image by pruning outlier'. Together they form a unique fingerprint.

Cite this