Image denoising with an orientation-adaptive Gaussian scale mixture model

D K Hammond and E P Simoncelli

Published in Proc 13th IEEE Int'l Conf on Image Proc (ICIP), pp. 1433--1436, Oct 2006.
© IEEE Computer Society

DOI: 10.1109/ICIP.2006.312699

This paper has been superseded by:
Image modeling and denoising with orientation-adapted Gaussian scale mixtures
D K Hammond and E P Simoncelli.
IEEE Trans. Image Processing, vol.17(11), pp. 2089--2101, Nov 2008.


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    • We develop a statistical model for images that explicitly captures variations in local orientation and contrast. Patches of wavelet coefficients are described as samples of a fixed Gaussian process that are rotated and scaled according to a set of hidden variables representing the local image contrast and orientation. An optimal Bayesian least squares estimator is developed by conditioning upon and integrating over the hidden orientation and scale variables. The resulting denoising procedure gives results that are visually superior to those obtained with a Gaussian scale mixture model that does not explicitly incorporate local image orientation.
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