Scale Mixtures of Gaussians and the Statistics of Natural Images
Presented (as a poster) at:
Neural Information Processing Systems, Denver CO, Dec 1999.
Published in:
Advances in Neural Information Processing Systems 12
ed. S.A. Solla, T.K. Leen, and K.-R. Müller, pp. 855-861.
© MIT Press, Cambridge, MA, 2000.
The statistics of photographic images, when represented using
multi-scale (wavelet) bases, exhibit two striking types of
non-Gaussian behavior. First, the marginal densities of the
coefficients have extended heavy tails. Second, the joint densities
exhibit variance dependencies not captured by second-order models.
We examine properties of the class of Gaussian scale mixtures,
and show that these densities can accurately characterize both the
marginal and joint distributions of natural image wavelet
coefficients. This class of model suggests a Markov structure, in
which wavelet coefficients are linked by hidden scaling variables
corresponding to local image structure. We derive an estimator for
these hidden variables, and show that a nonlinear "normalization"
procedure can be used to Gaussianize the coefficients.
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