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Learning Gaussian Conditional Random Fields for Low-Level Vision下载
资源介绍
Markov Random Field (MRF) models are a popular tool
for vision and image processing. Gaussian MRF models
are particularly convenient to work with because they can
be implemented using matrix and linear algebra routines.
However, recent research has focused on on discrete-valued
and non-convex MRF models because Gaussian models
tend to over-smooth images and blur edges. In this paper,
we show how to train a Gaussian Conditional Random Field
(GCRF) model that overcomes this weakness and can outperform the non-convex Field of Experts model on the task
of denoising images. A key advantage of the GCRF model is
that the parameters of the model can be optimized efficiently
on relatively large images. The competitive performance of
the GCRF model and the ease of optimizing its parameters
make the GCRF model an attractive option for vision and
image processing applications.