NDLI: Nonedge-Specific Adaptive Scheme for Highly Robust Blind Motion Deblurring of Natural Imagess

Content Provider	IEEE Xplore Digital Library
Author	Chao Wang Yong Yue Feng Dong Yubo Tao Xiangyin Ma Clapworthy, G. Hai Lin Xujiong Ye
Copyright Year	1992
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.
Sponsorship	IEEE Signal Processing Society
Page Count	14
File Size	3119898
Starting Page	884
Ending Page	897
File Format	PDF
ISSN	10577149
Volume Number	22
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Image edge detection Robustness Estimation Image restoration Deconvolution Educational institutions maximum a posteriori estimation Blind deconvolution image restoration
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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