Reference : Low-Light Image Enhancement of Permanently Shadowed Lunar Regions with Physics-Based ...
Scientific congresses, symposiums and conference proceedings : Poster
Engineering, computing & technology : Computer science
Low-Light Image Enhancement of Permanently Shadowed Lunar Regions with Physics-Based Machine Learning
moseley, ben [University of Oxford]
Bickel, Valentin [ETH Zurich/MPS Goettingen]
Rana, Loveneesh mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Remote Sensing >]
Lopez-Francos, Ignacio [NASA Ames Research Center]
GPU Technology Conference 2021 (GTC 2021)
12–16 April 2021
[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

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