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Learning sparse multiscale image representations

Presented at
Neural Information
Processing Systems, Vancouver, December 2002.

We describe a method for learning sparse multiscale
image representations using a sparse prior distribution over the basis
function coefficients. The prior consists of a mixture of a Gaussian and
a Dirac delta function, and thus encourages coefficients to have exact
zero values. Coefficients for an image are computed by sampling from the
resulting posterior distribution with a Gibbs sampler. The learned basis
is similar to the Steerable Pyramid basis, and yields slightly higher SNR
for the same number of active coefficients. Denoising using the learned
image model is demonstrated for some standard test images, with results
that compare favorably with other denoising methods.

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