A Bayesian Restoration Approach for Hyperspectral Images

In this paper, a Bayesian restoration technique for multiple observations of hyperspectral (HS) images is presented. As a prototype problem, we assume that a low-spatial-resolution HS observation and a high-spatial-resolution multispectral (MS) observation of the same scene are available. The proposed approach applies a restoration on the HS image and a joint fusion with the MS image, accounting for the joint statistics with the MS image. The restoration is based on an expectation-maximization algorithm, which applies a deblurring step and a denoising step iteratively. The Bayesian framework allows to include spatial information from the MS image. To keep the calculation feasible, a practical implementation scheme is presented. The proposed approach is validated by simulation experiments for general HS image restoration and for the specific case of pansharpening. The experimental results of the proposed approach are compared with pure fusion and deconvolution results for performance evaluation.