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Learning High-Order MRF Priors of Color Images

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83919

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

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Zitation

McAuley, J., Caetano T, Smola, A., & Franz, M. (2006). Learning High-Order MRF Priors of Color Images. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), 617-624.


Zitierlink: http://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.