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Título: Multi-scale image inpainting with label selection based on local statistics
Asesor(es): Rodríguez Valderrama, Paúl Antonio
Campo OCDE: https://purl.org/pe-repo/ocde/ford#2.02.05
Fecha de publicación: 9-sep-2014
Institución: Pontificia Universidad Católica del Perú
Resumen: We 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.
Disciplina académico-profesional: Procesamiento de señales e imágenes digitales
Institución que otorga el grado o título: Pontificia Universidad Católica del Perú. Escuela de Posgrado
Grado o título: Maestro en Procesamiento de señales e imágenes digitales
Fecha de registro: 9-sep-2014



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