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Fields of Experts
Stefan Roth and Michael J. Black
International Journal of Computer Vision (IJCV), 82(2):205-229, April 2009.

Conference version:

Fields of Experts: A Framework for Learning Image Priors
Stefan Roth and Michael J. Black
IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 860-867, San Diego, California, June 2005.


Note:
This page as well as the links below are provided for purposes of reproducible research. Please refer to our CVPR 2010 paper and the accompanying code for an improved Fields-of-Experts model that substantially outperforms this prior work.


We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach provides a practical method for learning high-order Markov random field (MRF) models with potential functions that extend over large pixel neighborhoods. These clique potentials are modeled using the Product-of-Experts framework that uses non-linear functions of many linear filter responses. In contrast to previous MRF approaches all parameters, including the linear filters themselves, are learned from training data. We demonstrate the capabilities of this Field-of-Experts model with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the model is trained on a generic image database and is not tuned toward a specific application, we obtain results that compete with specialized techniques.

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