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Article: Iterative blind image deconvolution in space and frequency domains

TitleIterative blind image deconvolution in space and frequency domains
Authors
Issue Date1999
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml
Citation
Proceedings Of Spie - The International Society For Optical Engineering, 1999, v. 3650, p. 70-77 How to Cite?
AbstractIn image acquisition, the captured image is often the result of the object being convolved with a blur function. Deconvolution is necessary in order to undo the effects of the blur. However, in real life we may have very little knowledge of the blur, and therefore we have to perform blind deconvolution. One major challenge of existing iterative algorithms for blind deconvolution is the enforcement of the convolution constraint. In this paper we describe a method whereby this constraint can be much more easily implemented in the frequency domain. This is possible because of Parseval's theorem, which allows us to relate projection in the space and frequency domains. Our algorithm also incorporates regularization of the estimated image through the use of Wiener filters. The restored images are compared to the original and noisy blurred images, and we find that the restoration process indeed provides an enhancement in visual quality.
Persistent Identifierhttp://hdl.handle.net/10722/155105
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLam, Edmund Yen_US
dc.contributor.authorGoodman, Joseph Wen_US
dc.date.accessioned2012-08-08T08:31:53Z-
dc.date.available2012-08-08T08:31:53Z-
dc.date.issued1999en_US
dc.identifier.citationProceedings Of Spie - The International Society For Optical Engineering, 1999, v. 3650, p. 70-77en_US
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/155105-
dc.description.abstractIn image acquisition, the captured image is often the result of the object being convolved with a blur function. Deconvolution is necessary in order to undo the effects of the blur. However, in real life we may have very little knowledge of the blur, and therefore we have to perform blind deconvolution. One major challenge of existing iterative algorithms for blind deconvolution is the enforcement of the convolution constraint. In this paper we describe a method whereby this constraint can be much more easily implemented in the frequency domain. This is possible because of Parseval's theorem, which allows us to relate projection in the space and frequency domains. Our algorithm also incorporates regularization of the estimated image through the use of Wiener filters. The restored images are compared to the original and noisy blurred images, and we find that the restoration process indeed provides an enhancement in visual quality.en_US
dc.languageengen_US
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xmlen_US
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_US
dc.titleIterative blind image deconvolution in space and frequency domainsen_US
dc.typeArticleen_US
dc.identifier.emailLam, Edmund Y:elam@eee.hku.hken_US
dc.identifier.authorityLam, Edmund Y=rp00131en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0032666523en_US
dc.identifier.volume3650en_US
dc.identifier.spage70en_US
dc.identifier.epage77en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridLam, Edmund Y=7102890004en_US
dc.identifier.scopusauthoridGoodman, Joseph W=7402288924en_US

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