A Single-Frame Super-Resolution Innovative Approach

Ramirez Sosa Moran, M.I., Torres-Méndez, L.A. and Castelán, M. 2007. A Single-Frame Super-Resolution Innovative Approach. in: MICAI 2007: Advances in Artificial Intelligence Springer. pp. 640-649

Chapter titleA Single-Frame Super-Resolution Innovative Approach
AuthorsRamirez Sosa Moran, M.I., Torres-Méndez, L.A. and Castelán, M.
Abstract

Super-resolution refers to the process of obtaining a high resolution image from one or more low resolution images. In this work, we present a novel method for the super-resolution problem for the limited case, where only one image of low resolution is given as an input. The proposed method is based on statistical learning for inferring the high frequencies regions which helps to distinguish a high resolution image from a low resolution one. These inferences are obtained from the correlation between regions of low and high resolution that come exclusively from the image to be super-resolved, in term of small neighborhoods. The Markov random fields are used as a model to capture the local statistics of high and low resolution data when they are analyzed at different scales and resolutions. Experimental results show the viability of the method.

Keywordscomputer vision, image resolution, super resolution
Book titleMICAI 2007: Advances in Artificial Intelligence
Page range640-649
Year2007
PublisherSpringer
Publication dates
Published04 Nov 2007
ISBN9783540766315
ISSN0302-9743
Digital Object Identifier (DOI)doi:10.1007/978-3-540-76631-5_61

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