Pan-sharpening with a Bayesian nonparametric dictionary learning model

Pan-sharpening, a method for constructing high resolution images from low resolution observations, has recently been explored from the perspective of compressed sensing and sparse representation theory. We present a new pansharpening algorithm that uses a Bayesian nonparametric dictionary learning model to give an underlying sparse representation for image reconstruction. In contrast to existing dictionary learning methods, the proposed method infers parameters such as dictionary size, patch sparsity and noise variances. In addition, our regularization includes image constraints such as a total variation penalization term and a new gradient penalization on the reconstructed PAN image. Our method does not require high resolution multiband images for dictionary learning, which are unavailable in practice, but rather the dictionary is learned directly on the reconstructed image as part of the inversion process. We present experiments on several images to validate our method and compare with several other wellknown approaches.