DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
About
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.
Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro, Enrico Magli• 2020
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Multi-image Super-resolution | Proba-V (val) | NIR PSNR (dB)47.93 | 10 | |
| Multitemporal Super-Resolution | Proba-V NIR (val) | cPSNR47.93 | 10 | |
| Multitemporal Super-Resolution | Proba-V RED (val) | cPSNR50.08 | 10 |
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