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Nonedge-specific adaptive scheme for highly robust blind motion deblurring of natural imagess

  • Chao Wang
    ,
  • Yong Yue
    ,
  • Feng Dong
    ,
  • Yubo Tao
    ,
  • Xiangyin Ma
    ,
  • Gordon Clapworthy
Research Output: Contribution to journal Article Peer-review

Abstract

Blind motion deblurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. Although significant progress has been made on tackling this problem, existing methods, when applied to highly diverse natural images, are still far from stable. This paper focuses on the robustness of blind motion deblurring methods toward image diversity-a critical problem that has been previously neglected for years. We classify the existing methods into two schemes and analyze their robustness using an image set consisting of 1.2 million natural images. The first scheme is edge-specific, as it relies on the detection and prediction of large-scale step edges. This scheme is sensitive to the diversity of the image edges in natural images. The second scheme is nonedge-specific and explores various image statistics, such as the prior distributions. This scheme is sensitive to statistical variation over different images. Based on the analysis, we address the robustness by proposing a novel nonedge-specific adaptive scheme (NEAS), which features a new prior that is adaptive to the variety of textures in natural images. By comparing the performance of NEAS against the existing methods on a very large image set, we demonstrate its advance beyond the state-of-the-art.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 884-897

Journal (Volume, Issue Number)

IEEE Transactions on Image Processing (Volume 22, Issue 3)

Publication milestones

  • Published - 01/01/2013

Publication status

Published - 01/01/2013

ISSN

1057-7149

External Publication IDs

  • handle.net: 10547/275852
  • Scopus: 84873318136

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