Depth image super-resolution using multi-dictionary sparse representation

H. Zheng, A. Bouzerdoum, S. L. Phung

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

17 Citations (Scopus)

Abstract

In this paper, we propose a new depth super-resolution technique based on multiple dictionary learning. A novel dictionary selection method using basis pursuit is proposed to generate multiple dictionaries adaptively. A sparse representation of each low-resolution input patch is derived based on the learned dictionaries, and then used to reconstruct the corresponding high-resolution patch. Experimental results are presented which show that the proposed multi-dictionary scheme outperforms existing depth super-resolution methods.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
PublisherIEEE Computer Society
Pages957-961
Number of pages5
ISBN (Print)9781479923410
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 15 Sept 201318 Sept 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period15/09/1318/09/13

Keywords

  • basis pursuit
  • depth super-resolution
  • dictionary selection
  • multiple dictionaries
  • sparse representation

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