Learning high-order MRF priors of color images

Authors:
Julian J. McAuley;Tibério S. Caetano;Alex J. Smola;Matthias O. Franz
Affiliations:
National ICT Australia, Canberra, Australia and University of New South Wales, Sydney, Australia;National ICT Australia, Canberra, Australia and RSISE, Australian National University, Canberra, Australia;National ICT Australia, Canberra, Australia and RSISE, Australian National University, Canberra, Australia;Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
Venue:
ICML '06 Proceedings of the 23rd international conference on Machine learning
Year:
2006

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Abstract

In this paper, we use large neighborhood Markov random fields to learn rich prior models of color images. Our approach extends the monochromatic Fields of Experts model (Roth & Black, 2005a) to color images. In the Fields of Experts model, the curse of dimensionality due to very large clique sizes is circumvented by parameterizing the potential functions according to a product of experts. We introduce simplifications to the original approach by Roth and Black which allow us to cope with the increased clique size (typically 3x3x3 or 5x5x3 pixels) of color images. Experimental results are presented for image denoising which evidence improvements over state-of-the-art monochromatic image priors.