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dc.contributor.authorCHAUDHURI, SUBHASISen_US
dc.contributor.authorRAJAGOPALAN, ANen_US
dc.date.accessioned2008-11-21T10:31:56Zen_US
dc.date.accessioned2011-11-25T12:46:48Zen_US
dc.date.accessioned2011-12-26T13:08:47Zen_US
dc.date.accessioned2011-12-27T05:34:08Z-
dc.date.available2008-11-21T10:31:56Zen_US
dc.date.available2011-11-25T12:46:48Zen_US
dc.date.available2011-12-26T13:08:47Zen_US
dc.date.available2011-12-27T05:34:08Z-
dc.date.issued1998en_US
dc.identifier.citationIEEE Transactions on Image Processing 7(7), 1075-79en_US
dc.identifier.issn1057-7149en_US
dc.identifier.urihttp://dx.doi.org/10.1109/83.701169en_US
dc.identifier.urihttp://hdl.handle.net/10054/90en_US
dc.identifier.urihttp://dspace.library.iitb.ac.in/xmlui/handle/10054/90en_US
dc.description.abstractA maximum likelihood-based method is proposed for blur identification from multiple observations of a scene. When the relations among the blurring functions are known, the estimate of blur obtained using the proposed method is very good. Since direct computation of the likelihood function becomes difficult as the number of images increases, we propose an algorithm to compute the likelihood function recursively.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.subjectImage Processingen_US
dc.subjectMaximum Likelihood Estimationen_US
dc.subjectRecursive Estimationen_US
dc.titleA recursive algorithm for maximum likelihood-based identification of blur from multiple observationsen_US
dc.typeArticleen_US
dc.description.copyrightIEEEen_US
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