A Single-Frame Super-Resolution Innovative Approach

  • Luz A. Torres-Méndez
  • Marco I. Ramírez-Sosa Morán
  • Mario Castelán
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4827)


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.


Hide Node High Resolution Image Markov Random Field Markov Random Field Model Belief Propagation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Luz A. Torres-Méndez
    • 1
  • Marco I. Ramírez-Sosa Morán
    • 2
  • Mario Castelán
    • 1
  1. 1.CINVESTAV Campus Saltillo, Ramos Arizpe, Coahuila, 25900México
  2. 2.ITESM Campus Saltillo, Saltillo, Coahuila, 25270México

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