Pan-sharpening based on nonparametric Bayesian adaptive dictionary learning

Pan-sharpening based on compressed sensing (CS) theory has been widely studied in recent years. In this paper, we present a novel CS-based pan-sharpening method based on nonparametric Bayesian adaptive dictionary learning. In contrast to existing optimization methods, the proposed method adaptively infers parameters such as dictionary size, patch sparsity and noise variances. In addition, high resolution multiband images, which are unavailable in practice, are not required to learn the dictionary anymore. An IKONOS satellite image is employed to validate the method. Both visual results and quality metrics demonstrate that proposed method is able to achieve higher spatial and spectral resolution simultaneously, compared with other well-known methods.