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dc.contributor.authorXiao, Lei
dc.contributor.authorGregson, James
dc.contributor.authorHeide, Felix
dc.contributor.authorHeidrich, Wolfgang
dc.date.accessioned2015-06-04T12:02:47Z
dc.date.available2015-06-04T12:02:47Z
dc.date.issued2015-05-13
dc.identifier.citationStochastic Blind Motion Deblurring 2015:1 IEEE Transactions on Image Processing
dc.identifier.issn1057-7149
dc.identifier.issn1941-0042
dc.identifier.doi10.1109/TIP.2015.2432716
dc.identifier.urihttp://hdl.handle.net/10754/556419
dc.description.abstractBlind motion deblurring from a single image is a highly under-constrained problem with many degenerate solutions. A good approximation of the intrinsic image can therefore only be obtained with the help of prior information in the form of (often non-convex) regularization terms for both the intrinsic image and the kernel. While the best choice of image priors is still a topic of ongoing investigation, this research is made more complicated by the fact that historically each new prior requires the development of a custom optimization method. In this paper, we develop a stochastic optimization method for blind deconvolution. Since this stochastic solver does not require the explicit computation of the gradient of the objective function and uses only efficient local evaluation of the objective, new priors can be implemented and tested very quickly. We demonstrate that this framework, in combination with different image priors produces results with PSNR values that match or exceed the results obtained by much more complex state-of-the-art blind motion deblurring algorithms.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7106534
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.titleStochastic Blind Motion Deblurring
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journalIEEE Transactions on Image Processing
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of British Columbia, Department of Computer Science, Vancouver, V6T1Z4, Canada
kaust.personHeidrich, Wolfgang
refterms.dateFOA2018-06-14T07:59:36Z
dc.date.published-online2015-05-13
dc.date.published-print2015-10


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