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


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.


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Correspondence to Todd A Stephenson.

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Stephenson, T.A., Chen, T. Adaptive Markov Random Fields for Example-Based Super-resolution of Faces. EURASIP J. Adv. Signal Process. 2006, 031062 (2006).

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