Learning low-level vision (2000)
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| Venue: | International Journal of Computer Vision |
| Citations: | 382 - 25 self |
BibTeX
@ARTICLE{Freeman00learninglow-level,
author = {William T. Freeman and Egon C. Pasztor},
title = {Learning low-level vision},
journal = {International Journal of Computer Vision},
year = {2000},
volume = {40},
pages = {2000}
}
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Abstract
We show a learning-based method for low-level vision problems. We set-up a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently propagate image information. Monte Carlo simulations justify this approximation. We apply this to the \super-resolution " problem (estimating high frequency details from a low-resolution image), showing good results. For the motion estimation problem, we show resolution of the aperture problem and lling-in arising from application of the same probabilistic machinery.
