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THREE-DIMENSIONAL IMAGE RESTORATION USING ARMA MODELLING AND ML ESTIMATION.

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dc.contributor.author Tzafestas, SG en
dc.contributor.author Angelleli, A-M en
dc.date.accessioned 2014-03-01T02:47:45Z
dc.date.available 2014-03-01T02:47:45Z
dc.date.issued 1985 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/33314
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-0022237512&partnerID=40&md5=896f533cb31c73b4b542b0ec48e1d4dd en
dc.subject.other SIGNAL FILTERING AND PREDICTION en
dc.subject.other AUTOREGRESSIVE MOVING-AVERAGE PROCESSES en
dc.subject.other IMAGE RESTORATION en
dc.subject.other MAXIMUM-LIKELIHOOD ESTIMATION en
dc.subject.other IMAGE PROCESSING en
dc.title THREE-DIMENSIONAL IMAGE RESTORATION USING ARMA MODELLING AND ML ESTIMATION. en
heal.type conferenceItem en
heal.publicationDate 1985 en
heal.abstract Most recursive image enhancement-restoration techniques are based on the assumption that the autocovariance function of the original noise-free image is known or can be estimated beforehand. Recently, T. Katayama (1979) developed a technique that bypasses this assumption by using an ARMA model of the 2-D image field, combined with maximum-likelihood (ML) parameter estimation and appropriate filtering. Katayama's technique is here extended to 3-dimensional (volume) image fields, which are encountered in many practical situations. Some digital simulation results have been derived for artificial 3-dimensional fields, showing the feasibility of the method. en
heal.publisher IEEE, New York, NY, USA en
heal.journalName [No source information available] en
dc.identifier.spage 227 en
dc.identifier.epage 232 en


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