Unsupervised Deep Hyperspectral Image Super-Resolution

Abstract

This chapter presents the recent advanced deep unsupervised hyperspectral (HS) image super-resolution framework for automatically generating a high-resolution (HR) HS image from its low-resolution (LR) HS and high-resolution RGB observations without any external sample. We incorporate the deep learned priors of the underlying structure in the latent HR-HS image with the mathematical model for formulating the degradation procedures of the observed LR-HS and HR-RGB observations and introduce an unsupervised end-to-end deep prior learning network for robust HR-HS image recovery. Experiments on two benchmark datasets validated that the proposed method manifest very impressive performance, and is even better than most state-of-the-art supervised learning approaches.