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Image super-resolution framework with multi-channel constraints

  • Ci Wang*
  • , Ping Xue
  • , Weisi Lin
  • , Minmin Shen
  • *Corresponding author for this work
  • Nanyang Technological University

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

Abstract

Super-resolution reconstruction (SR) has been widely used to produce a high resolution (HR) image from several low resolution (LR) ones. In current methods, a LR image is selected as benchmark and upsampled as the initial SR estimate. This SR estimate is then degraded and compared with the adjacent LR frames for correction. Considering LR images captured from the same HR image with different translation at different instants, SR outputs by different benchmark selection should be identical, so tighter constraints can be designed to limit SR indetermination and to produce better SR images. In this paper, we propose a novel SR framework and prove its efficiency statistically using the unbiased estimation. Experimental results indicate that the proposed algorithm outperforms some existing approaches in both subjective and objective terms.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
PublisherIEEE Computer Society
Pages456-459
Number of pages4
ISBN (Print)1424410177, 9781424410170
DOIs
StatePublished - 2007
Externally publishedYes
EventIEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China
Duration: 2 Jul 20075 Jul 2007

Publication series

NameProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

Conference

ConferenceIEEE International Conference onMultimedia and Expo, ICME 2007
Country/TerritoryChina
CityBeijing
Period2/07/075/07/07

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