Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1705
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dc.contributor31249es_ES
dc.contributor49390es_ES
dc.contributor123645es_ES
dc.contributor20608-
dc.contributor.otherhttps://orcid.org/0000-0002-7337-8974-
dc.contributor.otherhttps://orcid.org/0000-0003-1519-7718-
dc.coverage.spatialGlobales_ES
dc.creatorDe la Rosa Vargas, José Ismael-
dc.creatorVilla Hernández, José de Jesús-
dc.creatorGonzález Ramírez, Efrén-
dc.creatorDe la Rosa Miranda, Enrique-
dc.creatorGutiérrez, Osvaldo-
dc.creatorOlvera Olvera, Carlos Alberto-
dc.creatorCastañeda Miranda, Rodrígo-
dc.creatorFleury, Gilles-
dc.date.accessioned2020-04-16T18:13:23Z-
dc.date.available2020-04-16T18:13:23Z-
dc.date.issued2013-10-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn1990-2573es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1705-
dc.identifier.urihttps://doi.org/10.48779/8qr8-5610-
dc.description.abstractThe present work introduces an alternative method to deal with digital image restoration into a Bayesian framework, particularly, the use of a new half-quadratic function is proposed which performance is satisfactory compared with respect to some other functions in existing literature. The bayesian methodology is based on the prior knowledge of some information that allows an efficient modelling of the image acquisition process. The edge preservation of objects into the image while smoothing noise is necessary in an adequate model. Thus, we use a convexity criteria given by a semi-Huber function to obtain adequate weighting of the cost functions (half-quadratic) to be minimized. The principal objective when using Bayesian methods based on the Markov Random Fields (MRF) in the context of image processing is to eliminate those effects caused by the excessive smoothness on the reconstruction process of image which are rich in contours or edges. A comparison between the new introduced scheme and other three existing schemes, for the cases of noise filtering and image deblurring, is presented. This collection of implemented methods is inspired of course on the use of MRFs such as the semi-Huber, the generalized Gaussian, the Welch, and Tukey potential functions with granularity control. The obtained results showed a satisfactory performance and the effectiveness of the proposed estimator with respect to other three estimators.es_ES
dc.language.isoenges_ES
dc.publisherEuropean Optical Societyes_ES
dc.publisherSpringeres_ES
dc.relationhttp://www.jeos.org/index.php/jeos_rp/article/view/13072es_ES
dc.relation.urigeneralPublices_ES
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. 13072 (12 pages), Oct. 2013.es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherDigital image processinges_ES
dc.subject.otherimage filteringes_ES
dc.subject.otherimage deblurringes_ES
dc.subject.otherMarkov random fields (MRF)es_ES
dc.subject.otherhalf-quadratic functionses_ES
dc.titleSemi-Huber Half Quadratic Function and Comparative Study of Some MRFs for Bayesian Image Restorationes_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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