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dc.contributor.authorMoreno, J.J.-
dc.contributor.authorMartinez-Sanchez, Antonio-
dc.contributor.authorMartinez, J.A.-
dc.contributor.authorGarzón, E.M.-
dc.contributor.authorFernández, J.J.-
dc.contributor.otherFacultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Ingeniería de la Información y las Comunicaciones-
dc.date.accessioned2023-12-18T13:15:39Z-
dc.date.available2023-12-18T13:15:39Z-
dc.date.issued2018-05-29-
dc.identifier.citationBioinformatics, 34(21), 2018, 3776–3778-
dc.identifier.urihttp://hdl.handle.net/10201/136713-
dc.description© 2018. This document is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the accepted version of a published Work that appeared in final form in Bioinformatics . To access the final edited and published work see https://doi.org/10.1093/bioinformatics/bty435-
dc.description.abstractTomoEED is an optimized software tool for fast feature-preserving noise filtering of large 3D tomographic volumes on CPUs and GPUs. The tool is based on the anisotropic nonlinear diffusion method. It has been developed with special emphasis in the reduction of the computational demands by using different strategies, from the algorithmic to the high performance computing perspectives. TomoEED manages to filter large volumes in a matter of minutes in standard computers.es
dc.formatapplication/pdfes
dc.format.extent3-
dc.languageenges
dc.publisherOxford Academic-
dc.relationGrants TIN2015-66680 and SAF2017-84565-R (AEI/FEDER, UE) and Fundación Ramón Areces.es
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleTomoEED: fast edge-enhancing denoising of tomographic volumeses
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doihttps://doi.org/10.1093/bioinformatics/bty435-
Aparece en las colecciones:Artículos: Ingeniería de la Información y las Comunicaciones

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