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Understanding and evaluating blind deconvolution algorithms

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dc.contributor.advisor William Freeman
dc.contributor.author Freeman, William en_US
dc.contributor.author Durand, Fredo en_US
dc.contributor.author Weiss, Yair en_US
dc.contributor.author Levin, Anat en_US
dc.contributor.other Vision en_US
dc.date.accessioned 2009-03-31T18:00:05Z
dc.date.available 2009-03-31T18:00:05Z
dc.date.issued 2009-03-31
dc.identifier.uri http://hdl.handle.net/1721.1/44964
dc.description.abstract Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand.The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally. We explain the previously reported failure of the naive MAP approach by demonstrating that it mostly favors no-blur explanations. On the other hand we show that since the kernel size is often smaller than the image size a MAP estimation of the kernel alone can be well constrained and accurately recover the true blur. The plethora of recent deconvolution techniques makes an experimental evaluation on ground-truth data important. We have collected blur data with ground truth and compared recent algorithms under equal settings. Additionally, our data demonstrates that the shift-invariant blur assumption made by most algorithms is often violated. en_US
dc.format.extent 44 p. en_US
dc.relation.ispartofseries MIT-CSAIL-TR-2009-014
dc.subject deconvolution en_US
dc.title Understanding and evaluating blind deconvolution algorithms en_US


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