Fast super-resolution based on weighted collaborative representation

Hailiang Li, Kin Man Lam

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

4 Citations (Scopus)

Abstract

Recently, collaborative representation (CR) has been proposed as an l2-norm least-square solution for image super-resolution with significantly less computation than the l1-norm version of the Sparse-Coding-based Super-Resolution (ScSR) without any sacrifice in terms of image quality. In this paper we propose a novel weighted collaborative representation (WCR) instead of the original CR model for single image super-resolution. Our proposed method can achieve more than a 0.2∼0.3 dB gain without requiring any additional cost compared to the original CR model. Moreover, we devise a hierarchicalclustering KD-tree searching scheme which can reduce the computational complexity on searching part in our WCR model from O(n) to O(n1/m), where n is the atom count and m is the number of layers, without any compromise of image quality.
Original languageEnglish
Title of host publication2014 19th International Conference on Digital Signal Processing, DSP 2014
PublisherIEEE
Pages914-918
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
Country/TerritoryHong Kong
CityHong Kong
Period20/08/1423/08/14

Keywords

  • Collaborative representation
  • Image super-resolution
  • L<sup>2</sup>-norm
  • Ridge regression
  • Sparse coding

ASJC Scopus subject areas

  • Signal Processing

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