Single image super resolution based on sparse representation and adaptive dictionary selection

Chang Hong Fu, Hongli Chen, Hongbin Zhang, Yui Lam Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

An improved single image super resolution based on patch-wise sparse recovery is proposed in this paper. K-SVD is adopted to train a coupled dictionary. Besides, adaptive selection is proposed among dictionaries with different patch size. Simulation results show that the proposed approach provides good subjective quality and up to 0.4 dB PSNR improvement with significant time reduction.
Original languageEnglish
Title of host publication2014 19th International Conference on Digital Signal Processing, DSP 2014
PublisherIEEE
Pages449-453
Number of pages5
Volume2014-January
ISBN (Electronic)9781479946129
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 19th International Conference on Digital Signal Processing, DSP 2014 - Hong Kong, Hong Kong
Duration: 20 Aug 201423 Aug 2014

Conference

Conference2014 19th International Conference on Digital Signal Processing, DSP 2014
CountryHong Kong
CityHong Kong
Period20/08/1423/08/14

Keywords

  • Adpative dictionary selection
  • Dictionary learning
  • K-svd
  • Sparse representation
  • Super resolution

ASJC Scopus subject areas

  • Signal Processing

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