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.
@article{arxiv.2001.06342,
title = {DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images},
author = {Andrea Bordone Molini and Diego Valsesia and Giulia Fracastoro and Enrico Magli},
journal= {arXiv preprint arXiv:2001.06342},
year = {2020}
}
Comments
arXiv admin note: text overlap with arXiv:1907.06490