Reference : Low-light image enhancement of permanently shadowed lunar regions with physics-based ...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/45541
Low-light image enhancement of permanently shadowed lunar regions with physics-based machine learning
English
Moseley, Ben [University of Oxford]
Bikel, Valentin [ETH Zurich > > > ; MPS Goettingen]
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 mailto [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]
Dec-2020
Low-light image enhancement of permanently shadowed lunar regions with physics-based machine learning
Yes
Conference on Neural Information Processing Systems, NeurIPS
from 6-12-2020 to 12-12-2020
remote
[en] Machine learning ; Remote sensing ; Lunar exploration
[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.
http://hdl.handle.net/10993/45541

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