  Hyperspectral images are of crucial importance in order to better understand
features of different materials. To reach this goal, they leverage on a high
number of spectral bands. However, this interesting characteristic is often
paid by a reduced spatial resolution compared with traditional multispectral
image systems. In order to alleviate this issue, in this work, we propose a
simple and efficient architecture for deep convolutional neural networks to
fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution
multispectral image (HR-MSI), yielding a high-resolution hyperspectral image
(HR-HSI). The network is designed to preserve both spatial and spectral
information thanks to an architecture from two folds: one is to utilize the
HR-HSI at a different scale to get an output with a satisfied spectral
preservation; another one is to apply concepts of multi-resolution analysis to
extract high-frequency information, aiming to output high quality spatial
details. Finally, a plain mean squared error loss function is used to measure
the performance during the training. Extensive experiments demonstrate that the
proposed network architecture achieves best performance (both qualitatively and
quantitatively) compared with recent state-of-the-art hyperspectral image
super-resolution approaches. Moreover, other significant advantages can be
pointed out by the use of the proposed approach, such as, a better network
generalization ability, a limited computational burden, and a robustness with
respect to the number of training samples.
