Exploiting self-similarities for single frame super-resolution

Chih Yuan Yang, Jia Bin Huang, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

114 Citations (Scopus)

Abstract

We propose a super-resolution method that exploits self-similarities and group structural information of image patches using only one single input frame. The super-resolution problem is posed as learning the mapping between pairs of low-resolution and high-resolution image patches. Instead of relying on an extrinsic set of training images as often required in example-based super-resolution algorithms, we employ a method that generates image pairs directly from the image pyramid of one single frame. The generated patch pairs are clustered for training a dictionary by enforcing group sparsity constraints underlying the image patches. Super-resolution images are then constructed using the learned dictionary. Experimental results show the proposed method is able to achieve the state-of-the-art performance.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
Pages497-510
Number of pages14
EditionPART 3
DOIs
Publication statusPublished - 2011
Event10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: 2010 Nov 82010 Nov 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6494 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Asian Conference on Computer Vision, ACCV 2010
CountryNew Zealand
CityQueenstown
Period10/11/810/11/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yang, C. Y., Huang, J. B., & Yang, M. H. (2011). Exploiting self-similarities for single frame super-resolution. In Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers (PART 3 ed., pp. 497-510). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6494 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-19318-7_39