  Deep learning methods for super-resolution of a remote sensing scene from
multiple unregistered low-resolution images have recently gained attention
thanks to a challenge proposed by the European Space Agency. This paper
presents an evolution of the winner of the challenge, showing how incorporating
non-local information in a convolutional neural network allows to exploit
self-similar patterns that provide enhanced regularization of the
super-resolution problem. Experiments on the dataset of the challenge show
improved performance over the state-of-the-art, which does not exploit
non-local information.
