Phil Sallee, Bruno Olshausen
We describe a method for learning sparse multiscale image repre- sentations using a sparse prior distribution over the basis function coe(cid:14)cients. The prior consists of a mixture of a Gaussian and a Dirac delta function, and thus encourages coe(cid:14)cients to have exact zero values. Coe(cid:14)cients 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 coe(cid:14)cients. De- noising using the learned image model is demonstrated for some standard test images, with results that compare favorably with other denoising methods.