2pm, Tuesday, 11 May 2006, in Meyer 1024:

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An orientation-adaptive Gaussian scale mixture model for image denoising

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David Hammond
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LCV

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We develop a model for patches of image wavelet coefficients that
is explicitly adapted to local orientation. Image patches are
described as samples of a Gaussian process that is rotated and
scaled by hidden random variables representing the local image
orientation and contrast, respectively.  A Bayesian denoising
method, based on conditioning on and integrating over the hidden
variables, yields visually superior results when compared to
previous scale mixture models that do not explicitly model
orientation.


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