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Fields of experts: A framework for learning image priors (2005)

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by Stefan Roth , Michael J. Black
Venue:In CVPR
Citations:292 - 4 self
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BibTeX

@INPROCEEDINGS{Roth05fieldsof,
    author = {Stefan Roth and Michael J. Black},
    title = {Fields of experts: A framework for learning image priors},
    booktitle = {In CVPR},
    year = {2005}
}

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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 extends traditional Markov Random Field (MRF) models by learning potential functions over extended pixel neighborhoods. Field potentials are modeled using a Products-of-Experts framework that exploits nonlinear 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 and even outperform specialized techniques. 1.

Keyphrases

image prior    field potential    products-of-experts framework    previous mrf    expressive image prior    potential function    traditional markov random field    linear filter    many linear filter response    approximate inference scheme    extended pixel neighborhood    machine vision task    image denoising    generic image database    specific application    nonlinear function    expert model    image inpainting    example application    natural scene   

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