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.
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images / Molini, Andrea Bordone; Valsesia, Diego; Fracastoro, Giulia; Magli, Enrico. - (2020), pp. 3644-3656. (Intervento presentato al convegno International Geoscience and Remote Sensing Symposium, IGARSS 2020) [10.1109/IGARSS39084.2020.9324418].
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Molini, Andrea Bordone;Valsesia, Diego;Fracastoro, Giulia;Magli, Enrico
2020
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.File | Dimensione | Formato | |
---|---|---|---|
DeepSum_gconv.pdf
accesso aperto
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
Pubblico - Tutti i diritti riservati
Dimensione
816.01 kB
Formato
Adobe PDF
|
816.01 kB | Adobe PDF | Visualizza/Apri |
Valsesia-Deepsum1.pdf
accesso riservato
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
810.31 kB
Formato
Adobe PDF
|
810.31 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2844384