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Contextual dictionaries for image super resolution

  • School of Computer Science and Technology, Harbin Institute of Technology

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

Abstract

Traditional super resolution (SR) methods based on sparse representation have shown their excellent performance, however, the methods usually perform even worse when the input images and the training samples are diverse. Considering this problem, this paper presents a novel super resolution method based on sparse representation with contextual dictionary. Through adopting discriminative features instead of common features, the method train and use contextual dictionary in the SR process. Additionally, the method uses the first-order and second-order gradients of patch as representation, which ensures the neighbor information is introduced in the SR processing. The experiment results demonstrate the performance of this method has been promoted than other traditional method.

Original languageEnglish
Title of host publicationICIMCS 2011 - 3rd International Conference on Internet Multimedia Computing and Service, Proceedings
Pages150-153
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011 - Chengdu, China
Duration: 5 Aug 20117 Aug 2011

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011
Country/TerritoryChina
CityChengdu
Period5/08/117/08/11

Keywords

  • K-SVD
  • contextual dictionary
  • sparse representation
  • super resolution

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