Harmonic Analysis and Signal Processing Seminar
Image Denoising with an Orientation-Adaptive Gaussian Scale Mixture Model
David Hammond, CIMS
Tuesday, December 12, 2006, 2-3:00pm, WWH 613
Abstract
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.