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Learning High-Order MRF Priors of Color Images

MPG-Autoren
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Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://dl.acm.org/citation.cfm?doid=1143844.1143922
(Verlagsversion)

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ICML-2006-McAuley.pdf
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Zitation

McAuley, J., Caetano, T., Smola, A., & Franz, M. (2006). Learning High-Order MRF Priors of Color Images. In W. Cohen, & A. Moore (Eds.), ICML '06: Proceedings of the 23rd International Conference on Machine Learning (pp. 617-624). New York, NY, USA: ACM Press.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-D13B-D
Zusammenfassung
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 and Blackwell, 2005 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 several
simplifications of 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.