Sparse representation based iterative incremental image deblurring

Haichao Zhang, Yanning Zhang

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

22 Scopus citations

Abstract

Inspired by the observation that in image restoration, parametric models are extremely specific while pixel-level models are too loose, tending to under or over fit the underlying image respectively, in this paper, we proposed an 'intermediate-language' based method for image deblurring. The solution space is represented at a level higher than the pixel-grid level, while retain an enough degree of freedom (DOF), thus avoids the common local under or over fitting problem. Considering the sparseness property of images, a sparse representation based incremental iterative method is established for blurry image restoration. Comprehensive experiments demonstrate that the framework integrating the sparseness property of images significantly improves the deblurring performance.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages1293-1296
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: 7 Nov 200910 Nov 2009

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2009 IEEE International Conference on Image Processing, ICIP 2009
Country/TerritoryEgypt
CityCairo
Period7/11/0910/11/09

Keywords

  • Image deblurring
  • Image restoration
  • Sparse representation

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