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dc.contributor.advisorRodríguez Valderrama, Paúl Antonioes_ES
dc.contributor.authorParedes Zevallos, Daniel Leoncioes_ES
dc.description.abstractWe proposed a novel inpainting method where we use a multi-scale approach to speed up the well-known Markov Random Field (MRF) based inpainting method. MRF based inpainting methods are slow when compared with other exemplar-based methods, because its computational complexity is O(jLj2) (L feasible solutions’ labels). Our multi-scale approach seeks to reduces the number of the L (feasible) labels by an appropiate selection of the labels using the information of the previous (low resolution) scale. For the initial label selection we use local statistics; moreover, to compensate the loss of information in low resolution levels we use features related to the original image gradient. Our computational results show that our approach is competitive, in terms reconstruction quality, when compare to the original MRF based inpainting, as well as other exemplarbased inpaiting algorithms, while being at least one order of magnitude faster than the original MRF based inpainting and competitive with exemplar-based inpaiting.es_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Perú*
dc.sourcePontificia Universidad Católica del Perúes_ES
dc.sourceRepositorio de Tesis - PUCPes_ES
dc.subjectProcesamiento de imágenes digitaleses_ES
dc.subjectProcesos estocásticoses_ES
dc.titleMulti-scale image inpainting with label selection based on local statisticses_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ESíster en Procesamiento de señales e imágenes digitaleses_ESíaes_ES Universidad Católica del Perú. Escuela de Posgradoes_ES de señales e imágenes digitaleses_ES

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Atribución-NoComercial-SinDerivadas 2.5 Perú
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 2.5 Perú