Blind image deblurring via coupled sparse representation

Ming Yin, Junbin Gao, David Tien, Shuting Cai

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
46 Downloads (Pure)

Abstract

The problem of blind image deblurring is more challenging than that of non-blind image deblurring, due to the lack of knowledge about the point spread function in the imaging process. In this paper, a learning-based method of estimating blur kernel under the ℓ0 regularization sparsity constraint is proposed for blind image deblurring. Specifically, we model the patch-based matching between the blurred image and its sharp counterpart via a coupled sparse representation. Once the blur kernel is obtained, a non-blind deblurring algorithm can be applied to the final recovery of the sharp image. Our experimental results show that the visual quality of restored sharp images is competitive with the state-of-the-art algorithms for both synthetic and real images.
Original languageEnglish
Pages (from-to)814-821
Number of pages8
JournalJournal of Visual Communication and Image Representation
Volume25
Issue number5
DOIs
Publication statusPublished - Jul 2014

Grant Number

  • DP130100364

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