Image denoising using a local Gaussian scale mixture model in the wavelet domain

Vasily Strela; Javier Portilla; Eero P. Simoncelli

doi:10.1117/12.408621

4 December 2000 Image denoising using a local Gaussian scale mixture model in the wavelet domain

Vasily Strela, Javier Portilla, Eero P. Simoncelli

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408621
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

The statistics of photographic images, when decomposed in a multiscale wavelet basis, exhibit striking non-Gaussian behaviors. The joint densities of clusters of wavelet coefficients are well-described as a Gaussian scale mixture: a jointly Gaussian vector multiplied by a hidden scaling variable. We develop a maximum likelihood solution for estimating the hidden variable from an observation of the cluster of coefficients contaminated by additive Gaussian noise. The estimated hidden variable is then used to estimate the original noise-free coefficients. We demonstrate the power of this model through numerical simulations of image denoising.

Citation Download Citation

Vasily Strela, Javier Portilla, and Eero P. Simoncelli "Image denoising using a local Gaussian scale mixture model in the wavelet domain", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408621

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