[en] Finding water(-ice) on the Moon is key to enabling a sustainable human presence on
the Moon and beyond. There is evidence that water-ice is abundant in and around
the Moon’s Permanently Shadowed Regions (PSRs), however, direct visual detection
has not yet been possible. Surface ice or related physical features could potentially
be directly detected from high-resolution optical imagery, but, due to the extremely
low-light conditions in these areas, high levels of sensor and photon noise make this
very challenging. In this work we generate high-resolution, low-noise optical images
over lunar PSRs by using two physics-based deep neural networks to model and remove
CCD-related and photon noise in existing low-light optical imagery, potentially paving
the way for a direct water-ice detection method.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Moseley, Ben; University of Oxford
Bickel, Valentin; MPS Goettingen & ETH Zurich
Lopez-Francos, Ignacio; NASA Ames Research Center
RANA, Loveneesh ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Remote Sensing
OLIVARES MENDEZ, Miguel Angel ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Space Robotics
Wingo, Dennis; Skycorp Inc.
Zuniga, Allison; NASA Ames Research Center
Subtil, Nuno; Nvidia
D’Eon, Eugene; Nvidia
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Low-light image enhancement of permanently shadowed lunar regions with physics-based machine learning
Date de publication/diffusion :
décembre 2020
Nom de la manifestation :
Conference on Neural Information Processing Systems, NeurIPS
Date de la manifestation :
from 6-12-2020 to 12-12-2020
Titre de l'ouvrage principal :
Low-light image enhancement of permanently shadowed lunar regions with physics-based machine learning
