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A Kullback-Leibler divergence approach to blind image restoration

Seghouane, Abd-Krim

Description

A new algorithm for wavelet-based blind image restoration is presented in this paper. It is obtained by defining an intermediate variable to characterize the original image. Both the original image and the additive noise are modeled by multivariate Gaussian process. The blurring process is specified by its point spread function, which is unknown. The original image and the blur are estimated by alternating minimization of the KullbackLeibler divergence between a model family of probability...[Show more]

dc.contributor.authorSeghouane, Abd-Krim
dc.date.accessioned2015-12-10T23:36:20Z
dc.identifier.issn1057-7149
dc.identifier.urihttp://hdl.handle.net/1885/70090
dc.description.abstractA new algorithm for wavelet-based blind image restoration is presented in this paper. It is obtained by defining an intermediate variable to characterize the original image. Both the original image and the additive noise are modeled by multivariate Gaussian process. The blurring process is specified by its point spread function, which is unknown. The original image and the blur are estimated by alternating minimization of the KullbackLeibler divergence between a model family of probability distributions defined using a linear image model and a desired family of probability distributions constrained to be concentrated on the observed data. The intermediate variable is used to introduce regularization in the algorithm. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.sourceIEEE Transactions on Image Processing
dc.subjectKeywords: Alternating minimization; Blind image deconvolution; Blind image restoration; Closed-form expression; Gaussian scale mixtures; Kullback Leibler divergence; Kullback-Leibler information; Linear images; Multivariate Gaussian Process; Observed data; Original Blind image restoration; Gaussian scale mixture model; Kullback-Leibler information; wavelet denoising
dc.titleA Kullback-Leibler divergence approach to blind image restoration
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume20
dc.date.issued2011
local.identifier.absfor080106 - Image Processing
local.identifier.ariespublicationf2965xPUB2217
local.type.statusPublished Version
local.contributor.affiliationSeghouane, Abd-Krim, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.issue7
local.bibliographicCitation.startpage2078
local.bibliographicCitation.lastpage2083
local.identifier.doi10.1109/MLSP.2012.6349757
local.identifier.absseo970109 - Expanding Knowledge in Engineering
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2016-02-24T08:23:28Z
local.identifier.scopusID2-s2.0-84870706209
CollectionsANU Research Publications

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