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

  • Todd A StephensonEmail author
  • Tsuhan Chen
Open Access
Research Article
Part of the following topical collections:
  1. Super-Resolution Imaging: Analysis, Algorithms, and Applications


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 


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Copyright information

© 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.

Authors and Affiliations

  1. 1.Electrical & Computer Engineering DepartmentCarnegie Mellon UniversityPittsburghUSA
  2. 2.ReallaeR, LLCPort RepublicUSA

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