We develop a model for patches of image wavelet coefficients that is explicitly adapted to local orientation. Image patches are described as samples of a Gaussian process that is rotated and scaled by hidden random variables representing the local image orientation and contrast, respectively. A Bayesian denoising method, based on conditioning on and integrating over the hidden variables, yields visually superior results when compared to previous scale mixture models that do not explicitly model orientation.