@inproceedings{eee4ff71a0994015b20062bb14781cce,
title = "Image denoising with an orientation-adaptive Gaussian scale mixture model",
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.",
keywords = "Image processing, Image restoration",
author = "Hammond, {David K.} and Simoncelli, {Eero P.}",
year = "2006",
doi = "10.1109/ICIP.2006.312699",
language = "English (US)",
isbn = "1424404819",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "1433--1436",
booktitle = "2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings",
note = "2006 IEEE International Conference on Image Processing, ICIP 2006 ; Conference date: 08-10-2006 Through 11-10-2006",
}