DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1.
5 papers.
- 2022Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolutioncited by 40
- 2021Permutation invariance and uncertainty in multitemporal image super-resolutioncited by 18
- 2023Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 imagescited by 9
- 2022Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellitescited by 8
- 2024ESC-MISR: Enhancing Spatial Correlations for Multi-Image Super-Resolution in Remote Sensingcited by 0
- 2024ESC-MISR: Enhancing Spatial Correlations for Multi-Image Super-Resolution in Remote Sensing7 Nov 2024
- 2023Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images26 Jan 2023
- 2022Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution13 Jul 2022
- 2022Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites4 May 2022
- 2021Permutation invariance and uncertainty in multitemporal image super-resolution26 May 2021