2:00pm, Tuesday, 28 August 2007:
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Image denoising using a Mixture of Gaussian scale mixtures
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Jose Antonio Guerrero Colón
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Granada University
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We describe here two ways to improve on recent results in image restoration using Bayes least squares estimation
with local Gaussian scale mixtures (BLS-GSM) in overcomplete oriented pyramids. First one consists of allowing
for a spatial adaptation of the covariance matrix defining the GSM model at each pyramid subband. This can
be implemented in practice by dividing the subbands into spatial blocks. The other, more powerful, method is
to generalize the GSM model to include more than one covariance matrices for each subband. The advantage of
the latter method is its flexibility, as it allows for mixing Gaussian densities with different covariance matrices
at every spatial location in every subband. It also allows for non-local selective processing, taking advantage
of the repetition in the scene of image features that are not necessarily spatially grouped. Here we present mature results of the
spatially adaptive method applied to denoising, plus some estimation techniques and encouraging
preliminary results of the multi-GSM concept.