Image Denoising Using a Local Gaussian Scale Mixture Model
in the Wavelet Domain
Published in:
Proc 45th Annual Meeting of SPIE
San Diego, CA. July 2000.
© SPIE - the International Society for Optical Engineering, 2000.
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 (corresponding to basis
functions at nearby spatial positions, orientations and scales) 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.
Download
Full Text (218Kb, ps.gz)
/
Full Text (129Kb, pdf)
/ EPS Online Publications