  Enormous efforts have been recently made to super-resolve hyperspectral (HS)
images with the aid of high spatial resolution multispectral (MS) images. Most
prior works usually perform the fusion task by means of multifarious
pixel-level priors. Yet the intrinsic effects of a large distribution gap
between HS-MS data due to differences in the spatial and spectral resolution
are less investigated. The gap might be caused by unknown sensor-specific
properties or highly-mixed spectral information within one pixel (due to low
spatial resolution). To this end, we propose a subpixel-level HS
super-resolution framework by devising a novel decoupled-and-coupled network,
called DC-Net, to progressively fuse HS-MS information from the pixel- to
subpixel-level, from the image- to feature-level. As the name suggests, DC-Net
first decouples the input into common (or cross-sensor) and sensor-specific
components to eliminate the gap between HS-MS images before further fusion, and
then fully blends them by a model-guided coupled spectral unmixing (CSU) net.
More significantly, we append a self-supervised learning module behind the CSU
net by guaranteeing the material consistency to enhance the detailed
appearances of the restored HS product. Extensive experimental results show the
superiority of our method both visually and quantitatively and achieve a
significant improvement in comparison with the state-of-the-arts. Furthermore,
the codes and datasets will be available at
https://sites.google.com/view/danfeng-hong for the sake of reproducibility.
