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

Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion