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A Single-Frame Super-Resolution Innovative Approach

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MICAI 2007: Advances in Artificial Intelligence (MICAI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4827))

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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.

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Author information

Authors and Affiliations

  1. CINVESTAV Campus Saltillo, Ramos Arizpe, Coahuila, 25900, México

    Luz A. Torres-Méndez & Mario Castelán

  2. ITESM Campus Saltillo, Saltillo, Coahuila, 25270, México

    Marco I. Ramírez-Sosa Morán

Authors
  1. Luz A. Torres-Méndez
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  2. Marco I. Ramírez-Sosa Morán
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  3. Mario Castelán
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Editor information

Alexander Gelbukh Ángel Fernando Kuri Morales

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© 2007 Springer-Verlag Berlin Heidelberg

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Torres-Méndez, L.A., Ramírez-Sosa Morán, M.I., Castelán, M. (2007). A Single-Frame Super-Resolution Innovative Approach. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_61

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  • DOI: https://doi.org/10.1007/978-3-540-76631-5_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76630-8

  • Online ISBN: 978-3-540-76631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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