  Super-resolution reconstruction (SRR) is a process aimed at enhancing spatial
resolution of images, either from a single observation, based on the learned
relation between low and high resolution, or from multiple images presenting
the same scene. SRR is particularly valuable, if it is infeasible to acquire
images at desired resolution, but many images of the same scene are available
at lower resolution---this is inherent to a variety of remote sensing
scenarios. Recently, we have witnessed substantial improvement in single-image
SRR attributed to the use of deep neural networks for learning the relation
between low and high resolution. Importantly, deep learning has not been
exploited for multiple-image SRR, which benefits from information fusion and in
general allows for achieving higher reconstruction accuracy. In this letter, we
introduce a new method which combines the advantages of multiple-image fusion
with learning the low-to-high resolution mapping using deep networks. The
reported experimental results indicate that our algorithm outperforms the
state-of-the-art SRR methods, including these that operate from a single image,
as well as those that perform multiple-image fusion.
