No full text
Poster (Scientific congresses, symposiums and conference proceedings)
Low-Light Image Enhancement of Permanently Shadowed Lunar Regions with Physics-Based Machine Learning
moseley, ben; Bickel, Valentin; Rana, Loveneesh et al.
2021GPU Technology Conference 2021 (GTC 2021)
 

Files


Full Text
No document available.

Send to



Details



Abstract :
[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 :
Computer science
Author, co-author :
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
External co-authors :
yes
Language :
English
Title :
Low-Light Image Enhancement of Permanently Shadowed Lunar Regions with Physics-Based Machine Learning
Publication date :
2021
Event name :
GPU Technology Conference 2021 (GTC 2021)
Event organizer :
NVIDIA
Event date :
12–16 April 2021
Audience :
International
Available on ORBilu :
since 11 January 2022

Statistics


Number of views
105 (9 by Unilu)
Number of downloads
0 (0 by Unilu)

Bibliography


Similar publications



Contact ORBilu