  To overcome inherent hardware limitations of hyperspectral imaging systems
with respect to their spatial resolution, fusion-based hyperspectral image
(HSI) super-resolution is attracting increasing attention. This technique aims
to fuse a low-resolution (LR) HSI and a conventional high-resolution (HR) RGB
image in order to obtain an HR HSI. Recently, deep learning architectures have
been used to address the HSI super-resolution problem and have achieved
remarkable performance. However, they ignore the degradation model even though
this model has a clear physical interpretation and may contribute to improve
the performance. We address this problem by proposing a method that, on the one
hand, makes use of the linear degradation model in the data-fidelity term of
the objective function and, on the other hand, utilizes the output of a
convolutional neural network for designing a deep prior regularizer in spectral
and spatial gradient domains. Experiments show the performance improvement
achieved with this strategy.
