Bayesian fusion of hyperspectral and multispectral images using Gaussian scale mixture prior

In this paper, a wavelet-based Bayesian fusion framework is presented, in which a low spatial resolution hyperspectral (HS) image is fused with a high spatial resolution multi spectral (MS) image by accounting for the joint statistics. Particularly, a zero-mean heavy-tailed model, Gaussian Scale Mixture (GSM) model, is employed as the prior, which is believed to be capable of modelling the distribution of wavelet coefficients more accurately than traditional Gaussian model. To keep the calculations feasible, a practical implementation scheme is presented. The proposed approach is validated by simulation experiments for both general HS and MS image fusion as well as the specific case of pansharpening. The experimental results of the proposed approach are also compared with its counterpart employing a Gaussian prior for performance evaluation.