[en] We show that it is possible to significantly enhance the quality of extremely low-light images of permanently-shaded
regions (PSRs) on the moon by using two physics-based deep neural networks, which we called HORUS, to remove
CCD sensor-related noise and photon noise. To inform our distribution of training data, we perform ray tracing over a
digital elevation model of the moon and derive the distributions of secondary illumination angles in PSRs. Our network
provides high-resolution, low-noise images that will help enable future ground missions to plan and execute safe and
effective traverses into, around, and out of lunar PSRs — a critical step in our endeavor to explore the moon and
beyond.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
moseley, ben; University of Oxford
Bickel, Valentin; ETH Zurich/MPS Goettingen
RANA, Loveneesh ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Remote Sensing
Lopez-Francos, Ignacio; NASA Ames Research Center
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Low-Light Image Enhancement of Permanently Shadowed Lunar Regions with Physics-Based Machine Learning
