EURASIP Journal on Applied Signal Processing 
Volume 2006 (2006), Article ID 31062, 11 pages
doi:10.1155/ASP/2006/31062

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

Todd A. Stephenson1,2 and Tsuhan Chen1

 1Electrical & Computer Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh 15213-3890, PA, USA
2ReallaeR, LLC, P.O. Box 549, Port Republic 20676, MD, USA
Received 21 December 2004; Revised 1 April 2005; Accepted 5 April 2005

Abstract

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.