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Multi-scale blind motion deblurring using local minimum

  • Chao Wang
    ,
  • Li-Feng Sun
    ,
  • ZhuoYuan Chen
    ,
  • JianWei Zhang
    ,
  • ShiQiang Yang
Research Output: Contribution to journal Article Peer-review

Abstract

Blind deconvolution, a chronic inverse problem, is the recovery of the latent sharp image from a blurred one when the blur kernel is unknown. Recent algorithms based on the MAP approach encounter failures since the global minimum of the negative MAP scores really favors the blurry image. The goal of this paper is to demonstrate that the sharp image can be obtained from the local minimum by using the MAP approach. We first propose a cross-scale constraint to make the sharp image correspond to a good local minimum. Then the cross-scale initialization, iterative likelihood update and the iterative residual deconvolution are adopted to trap the MAP approach in the desired local minimum. These techniques result in our cross-scale blind deconvolution approach which constrains the solution from coarse to fine. We test our approach on the standard dataset and many other challenging images. The experimental results suggest that our approach outperforms all existing alternatives.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 01500

Journal (Volume, Issue Number)

Inverse Problems (Volume 26, Issue 1)

Publication milestones

  • Published - 01/01/2010

Publication status

Published - 01/01/2010

ISSN

0266-5611

External Publication IDs

  • handle.net: 10547/224106
  • Scopus: 74549163652

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