Single-image super-resolution via sparse coding regression

Yi Tang, Yuan Yuan, Pingkun Yan, Xuelong Li

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

15 Scopus citations

Abstract

In this paper, it has been shown that the sparse coding algorithm for single-image super-resolution is equivalent to a linear regression algorithm in the sparse coding space. Following the idea, the sparse coding algorithm are generalized by a novel L2-Boosting-based single-resolution super-resolution algorithm which focuses on the relationship between sparse codings corresponding to the low- and high-resolution image patches. The experimental results demonstrate the effectiveness of the proposed algorithm by comparing with other state-of-the- art algorithms.

Original languageEnglish
Title of host publicationProceedings - 6th International Conference on Image and Graphics, ICIG 2011
Pages267-272
Number of pages6
DOIs
StatePublished - 2011
Externally publishedYes
Event6th International Conference on Image and Graphics, ICIG 2011 - Hefei, Anhui, China
Duration: 12 Aug 201115 Aug 2011

Publication series

NameProceedings - 6th International Conference on Image and Graphics, ICIG 2011

Conference

Conference6th International Conference on Image and Graphics, ICIG 2011
Country/TerritoryChina
CityHefei, Anhui
Period12/08/1115/08/11

Keywords

  • Greedy regression
  • L-boosting
  • Sparse coding
  • Super-resolution

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