2.50
Hdl Handle:
http://hdl.handle.net/10547/275852
Title:
Nonedge-specific adaptive scheme for highly robust blind motion deblurring of natural imagess
Authors:
Wang, Chao; Yue, Yong; Dong, Feng; Tao, Yubo; Ma, Xiangyin; Clapworthy, Gordon J.; Lin, Hai; Ye, Xujiong
Other Titles:
Nonedge-specific adaptive scheme for highly robust blind motion deblurring of natural images
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.
Affiliation:
Department of Computer Science and Technology, University of Bedfordshire
Citation:
Wang C., Yue Y., Dong F., Tao Y., Ma X., Clapworthy G., Lin H., Ye X., (2012) 'Nonedge-Specific Adaptive Scheme for Highly Robust Blind Motion Deblurring of Natural Images', IEEE Transactions on Image Processing 22 (3):884-897
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Journal:
IEEE Transactions on Image Processing
Issue Date:
2013
URI:
http://hdl.handle.net/10547/275852
DOI:
10.1109/TIP.2012.2219548
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6305479
Type:
Article
Language:
en
ISSN:
1057-7149; 1941-0042
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Chaoen_GB
dc.contributor.authorYue, Yongen_GB
dc.contributor.authorDong, Fengen_GB
dc.contributor.authorTao, Yuboen_GB
dc.contributor.authorMa, Xiangyinen_GB
dc.contributor.authorClapworthy, Gordon J.en_GB
dc.contributor.authorLin, Haien_GB
dc.contributor.authorYe, Xujiongen_GB
dc.date.accessioned2013-03-25T12:38:56Z-
dc.date.available2013-03-25T12:38:56Z-
dc.date.issued2013-
dc.identifier.citationWang C., Yue Y., Dong F., Tao Y., Ma X., Clapworthy G., Lin H., Ye X., (2012) 'Nonedge-Specific Adaptive Scheme for Highly Robust Blind Motion Deblurring of Natural Images', IEEE Transactions on Image Processing 22 (3):884-897en_GB
dc.identifier.issn1057-7149-
dc.identifier.issn1941-0042-
dc.identifier.doi10.1109/TIP.2012.2219548-
dc.identifier.urihttp://hdl.handle.net/10547/275852-
dc.description.abstractBlind 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.en_GB
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6305479en_GB
dc.rightsArchived with thanks to IEEE Transactions on Image Processingen_GB
dc.subjectblind deconvolutionen_GB
dc.subjectimage restorationen_GB
dc.subjectmaximum a posteriori estimationen_GB
dc.titleNonedge-specific adaptive scheme for highly robust blind motion deblurring of natural imagessen
dc.title.alternativeNonedge-specific adaptive scheme for highly robust blind motion deblurring of natural imagesen_GB
dc.typeArticleen
dc.contributor.departmentDepartment of Computer Science and Technology, University of Bedfordshireen_GB
dc.identifier.journalIEEE Transactions on Image Processingen_GB
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