Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1708
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dc.contributor31249es_ES
dc.contributor20608-
dc.contributor.otherhttps://orcid.org/0000-0002-7337-8974-
dc.coverage.spatialGlobales_ES
dc.creatorDe la Rosa Vargas, José Ismael-
dc.creatorVilla Hernández, José de Jesús-
dc.creatorDe la Rosa Miranda, Enrique-
dc.creatorGonzález Ramírez, Efrén-
dc.creatorGutierrez, Osvaldo-
dc.creatorEscalante, Nivia-
dc.creatorIvanov, Rumen-
dc.creatorFleury, Gilles-
dc.date.accessioned2020-04-16T18:24:07Z-
dc.date.available2020-04-16T18:24:07Z-
dc.date.issued2013-07-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn1990-2573es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1708-
dc.identifier.urihttps://doi.org/10.48779/ge40-k565-
dc.description.abstractWe introduce a new approach for image filtering in a Bayesian framework. In this case the probability density function (pdf) of the likelihood function is approximated using the concept of non-parametric or kernel estimation. The method is based on the generalized Gaussian Markov random fields (GGMRF), a class of Markov random fields which are used as prior information into the Bayesian rule, which principal objective is to eliminate those effects caused by the excessive smoothness on the reconstruction process of images which are rich in contours or edges. Accordingly to the hypothesis made for the present work, it is assumed a limited knowledge of the noise pdf, so the idea is to use a non-parametric estimator to estimate such a pdf and then apply the entropy to construct the cost function for the likelihood term. The previous idea leads to the construction of Maximum a posteriori (MAP) robust estimators, since the real systems are always exposed to continuous perturbations of unknown nature. Some promising results of three new MAP entropy estimators (MAPEE) for image filtering are presented, together with some concluding remarks.es_ES
dc.language.isoenges_ES
dc.publisherEuropean Optical Societyes_ES
dc.relationhttps://www.jeos.org/index.php/jeos_rp/article/view/13047es_ES
dc.relation.urigeneralPublices_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América*
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.sourceJournal of the European Optical Society-Rapid Publication, Vol. 8, No. 13047, pp. 1-7es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherDigital image processinges_ES
dc.subject.otherimage recognitiones_ES
dc.subject.otheralgorithms and filterses_ES
dc.subject.otherimage reconstruction-restorationes_ES
dc.subject.otherinverse problemses_ES
dc.titleMAP entropy estimation: Applications in robust image filteringes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

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