DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images

Andrea Bordone Molini; Diego Valsesia; Giulia Fracastoro; Enrico Magli

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arXiv · 2001.06342

DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images

Andrea Bordone Molini·Diego Valsesia·Giulia Fracastoro·Enrico Magli

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Abstract

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.

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Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro, Enrico Magli. 2020-01-15. DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images. https://arxiv.org/abs/2001.06342

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