2pm, Tuesday, 14 Feb 2006:

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Image Denoising with an Orientation-Adaptive Gaussian Scale Mixture Model
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David Hammond
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LCV/NYU

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We develop a statistical model for images that explicitly captures
variations in local orientation and contrast.
Patches of wavelet coefficients are described
as samples of a fixed Gaussian process that are rotated and scaled
according to a set of hidden variables representing the local image
contrast and orientation.  An optimal Bayesian least squares
estimator is developed by conditioning upon and integrating over the
hidden orientation and scale variables. The resulting denoising
procedure gives results that are visually superior to those
obtained with a
Gaussian scale mixture model that does not explicitly incorporate local
image orientation.

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