Bayesian Sparse Representation for Hyperspectral Image Super Resolution

Despite the proven efficacy of hyperspectral imaging in many computer vision tasks, its widespread use is hindered by its low spatial resolution, resulting from hardware limitations. We propose a hyperspectral image super resolution approach that fuses a high resolution image with the low resolution hyperspectral image using non-parametric Bayesian sparse representation. The proposed approach first infers probability distributions for the material spectra in the scene and their proportions. The distributions are then used to compute sparse codes of the high resolution image. To that end, we propose a generic Bayesian sparse coding strategy to be used with Bayesian dictionaries learned with the Beta process. We theoretically analyze the proposed strategy for its accurate performance. The computed codes are used with the estimated scene spectra to construct the super resolution hyperspectral image. Exhaustive experiments on two public databases of ground based hyperspectral images and a remotely sensed image show that the proposed approach outperforms the existing state of the art.