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 ![]() | |
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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