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Fields of experts

 
: Roth, Stefan; Black, Michael

:

International Journal of Computer Vision 82 (2009), No.2, pp.205-229
ISSN: 0920-5691
ISSN: 1573-1405
English
Journal Article
Fraunhofer IGD ()
computer vision; Markov Random Field; MRF; machine learning; low-level image processing; image restoration

Abstract
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.

: http://publica.fraunhofer.de/documents/N-94120.html