Wavelet denoising of multicomponent images, using a Gaussian Scale Mixture model

In this paper, denoising on multicomponent images is performed. The presented procedure is a spatial wavelet-based denoising techniques, based on Bayesian least-squares optimization procedures, using a prior model for the wavelet coefficients that account for the inter-correlations between the multicomponent bands. The applied prior model for the multicomponent signal is a Gaussian scale mixture (GSM) model. The method is compared to single-band wavelet denoising and to multiband denoising using a Gaussian prior. Experiments on a Land-sat multispectral remote sensing image are conducted