Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1712
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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.creatorCortez, Joaquín-
dc.creatorGamboa Rosales, Hamurabi-
dc.creatorArceo Olague, José Guadalupe-
dc.creatorGonzález Ramírez, Efrén-
dc.date.accessioned2020-04-16T19:05:26Z-
dc.date.available2020-04-16T19:05:26Z-
dc.date.issued2018-01-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn1380-7501es_ES
dc.identifier.issn1573-7721es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1712-
dc.identifier.urihttps://doi.org/10.48779/1sqg-c547-
dc.description.abstractThe present work proposes a review and comparison of different Kernel functionals and neighborhood geometry for Nonlocal Means (NLM) in the task of digital image filtering. Some different alternatives to change the classical exponential kernel function used in NLM methods are explored. Moreover, some approaches that change the geometry of the neighborhood and use dimensionality reduction of the neighborhood or patches onto principal component analysis (PCA) are also analyzed, and their performance is compared with respect to the classic NLM method. Mainly, six approaches were compared using quantitative and qualitative evaluations, to do this an homogeneous framework has been established using the same simulation platform, the same computer, and same conditions for the initializing parameters.es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relationhttps://doi.org/10.1007/s11042- 016-4322-1es_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.sourceMultimedia Tools Applications, Vol. 77, No. 1, pp. 1205-1235es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherImage filteringes_ES
dc.subject.otherNonlocal meanses_ES
dc.subject.otherKernel functionalses_ES
dc.subject.otherSparse learning techniqueses_ES
dc.subject.otherCollaborative filteringes_ES
dc.titleOn the comparison of different Kernel functionals and neighborhood geometry for Nonlocal Means 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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