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. / Tesis
| Identifer | oai:union.ndltd.org:PUCP/oai:tesis.pucp.edu.pe:123456789/5578 |
| Date | 09 September 2014 |
| Creators | Paredes Zevallos, Daniel Leoncio |
| Contributors | Rodríguez Valderrama, Paúl Antonio |
| Publisher | Pontificia Universidad Católica del Perú |
| Source Sets | Pontificia Universidad Católica del Perú |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | Pontificia Universidad Católica del Perú, Repositorio de Tesis - PUCP |
| Rights | info:eu-repo/semantics/openAccess, Atribución-NoComercial-SinDerivadas 2.5 Perú, http://creativecommons.org/licenses/by-nc-nd/2.5/pe/ |
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