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

نویسندگان

  • Todd A. Stephenson
  • Tsuhan Chen
چکیده

Image enhancement of low-resolution images can be done throughmethods 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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عنوان ژورنال:
  • EURASIP J. Adv. Sig. Proc.

دوره 2006  شماره 

صفحات  -

تاریخ انتشار 2006