Centralized sparse representation for image restoration

Weisheng Dong, Lei Zhang, Guangming Shi

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

208 Citations (Scopus)

Abstract

This paper proposes a novel sparse representation model called centralized sparse representation (CSR) for image restoration tasks. In order for faithful image reconstruction, it is expected that the sparse coding coefficients of the degraded image should be as close as possible to those of the unknown original image with the given dictionary. However, since the available data are the degraded (noisy, blurred and/or down-sampled) versions of the original image, the sparse coding coefficients are often not accurate enough if only the local sparsity of the image is considered, as in many existing sparse representation models. To make the sparse coding more accurate, a centralized sparsity constraint is introduced by exploiting the nonlocal image statistics. The local sparsity and the nonlocal sparsity constraints are unified into a variational framework for optimization. Extensive experiments on image restoration validated that our CSR model achieves convincing improvement over previous state-of-the-art methods.

Original languageEnglish
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Pages1259-1266
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2011
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: 6 Nov 201113 Nov 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference2011 IEEE International Conference on Computer Vision, ICCV 2011
Country/TerritorySpain
CityBarcelona
Period6/11/1113/11/11

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this