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

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 031062 (2006) Cite this article

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.

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Authors and Affiliations

  1. Electrical & Computer Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213-3890, USA

    Todd A Stephenson & Tsuhan Chen

  2. ReallaeR, LLC, P.O. Box 549, Port Republic, MD, 20676, USA

    Todd A Stephenson

Authors
  1. Todd A Stephenson
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  2. Tsuhan Chen
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Correspondence to Todd A Stephenson.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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). https://doi.org/10.1155/ASP/2006/31062

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  • DOI: https://doi.org/10.1155/ASP/2006/31062

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