  Deep learning-based (DL-based) hyperspectral image (HIS) super-resolution
(SR) methods have achieved remarkable performance and attracted attention in
industry and academia. Nonetheless, most current methods explored and learned
the mapping relationship between low-resolution (LR) and high-resolution (HR)
HSIs, leading to the side effect of increasing unreliability and irrationality
in solving the ill-posed SR problem. We find, quite interestingly, LR imaging
is similar to the mixed pixel phenomenon. A single photodetector in sensor
arrays receives the reflectance signals reflected by a number of classes,
resulting in low spatial resolution and mixed pixel problems. Inspired by this
observation, this paper proposes a component-aware HSI SR network called
UnmixingSR, in which the unsupervised HU as an auxiliary task is used to
perceive the material components of HSIs. We regard HU as an auxiliary task and
incorporate it into the HSI SR process by exploring the constraints between LR
and HR abundances. Instead of only learning the mapping relationship between LR
and HR HSIs, we leverage the bond between LR abundances and HR abundances to
boost the stability of our method in solving SR problems. Moreover, the
proposed unmixing process can be embedded into existing deep SR models as a
plug-in-play auxiliary task. Experimental results on hyperspectral experiments
show that unmixing process as an auxiliary task incorporated into the SR
problem is feasible and rational, achieving outstanding performance. The code
is available at
