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Image denoising using learned overcomplete representations

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2 Author(s)
P. Sallee ; Dept. of Comput. Sci., UC Davis, CA, USA ; B. A. Olshausen

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. Denoising using the learned image model is demonstrated for some standard test images, with results that compare favorably with other denoising methods.

Published in:

Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on  (Volume:3 )

Date of Conference:

14-17 Sept. 2003