Image denoising with an orientation-adaptive Gaussian scale mixture model

D K Hammond and E P Simoncelli

Presented at:
13th IEEE Int'l Conference on Image Processing

Published in Proc 13th IEEE Int'l Conf on Image Proc, pp. 1433--1436, Oct 2006.
© IEEE Computer Society
DOI: 10.1109/ICIP.2006.312699

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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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