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  • Research Article
  • Open Access

Adaptive Markov Random Fields for Example-Based Super-resolution of Faces

EURASIP Journal on Advances in Signal Processing20062006:031062

  • Received: 21 December 2004
  • Accepted: 5 April 2005
  • Published:


Image enhancement of low-resolution images can be done through methods such as interpolation, super-resolution using multiple video frames, and example-based super-resolution. Example-based super-resolution, in particular, is suited to images that have a strong prior (for those frameworks that work on only a single image, it is more like image restoration than traditional, multiframe super-resolution). For example, hallucination and Markov random field (MRF) methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be. We propose to use even stronger prior information by extending MRF-based super-resolution to use adaptive observation and transition functions, that is, to make these functions region-dependent. We show with face images how we can adapt the modeling for each image patch so as to improve the resolution.


  • Information Technology
  • Quantum Information
  • Random Field
  • Transition Function
  • Prior Information

Authors’ Affiliations

Electrical & Computer Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890, USA
ReallaeR, LLC, P.O. Box 549, Port Republic, MD 20676, USA


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© T. A. Stephenson and T. Chen 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.