Adaptive Wiener Denoising using a
Gaussian Scale Mixture Model in the Wavelet Domain
Published in:
Proceedings of the 8th IEEE Int'l Conf on Image Processing
Thessaloniki, Greece. 7-10 October 2001.
doi: 10.1109/ICIP.2001.958418
© IEEE Computer Society.
Full-length papers on wavelet denoising:
•
Bayesian denoising of visual images in the wavelet domain, Spring 1999.
•
Image denoising using a scale mixture of Gaussians in the wavelet domain, Fall 2003.
We describe a statistical model for images decomposed
in an overcomplete wavelet pyramid. Each coefficient of the pyramid is
modeled as the product of two independent random variables: an element of
a Gaussian random field, and a hidden multiplier with a marginal
log-normal prior. The latter modulates the local variance of the
coefficients. We assume subband coefficients are contaminated with
additive Gaussian noise of known covariance,
and compute a MAP estimate of each multiplier variable based on
observation of a local neighborhood of coefficients.
Conditioned on this multiplier, we then estimate the subband coefficients
with a local Wiener estimator. Unlike previous approaches, we (a)
empirically motivate our choice for the prior on the multiplier; (b)
use the full covariance of signal and noise in the estimation; (c)
include adjacent scales in the conditioning neighborhood. To our
knowledge, the results are the best in the literature, both visually
and in terms of squared error.
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