  Hyperspectral image produces high spectral resolution at the sacrifice of
spatial resolution. Without reducing the spectral resolution, improving the
resolution in the spatial domain is a very challenging problem. Motivated by
the discovery that hyperspectral image exhibits high similarity between
adjacent bands in a large spectral range, in this paper, we explore a new
structure for hyperspectral image super-resolution (DualSR), leading to a
dual-stage design, i.e., coarse stage and fine stage. In coarse stage, five
bands with high similarity in a certain spectral range are divided into three
groups, and the current band is guided to study the potential knowledge. Under
the action of alternative spectral fusion mechanism, the coarse SR image is
super-resolved in band-by-band. In order to build model from a global
perspective, an enhanced back-projection method via spectral angle constraint
is developed in fine stage to learn the content of spatial-spectral
consistency, dramatically improving the performance gain. Extensive experiments
demonstrate the effectiveness of the proposed coarse stage and fine stage.
Besides, our network produces state-of-the-art results against existing works
in terms of spatial reconstruction and spectral fidelity.
