References of "Lopez-Francos, Ignacio"
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See detailLow-Light Image Enhancement of Permanently Shadowed Lunar Regions with Physics-Based Machine Learning
moseley, ben; Bickel, Valentin; Rana, Loveneesh UL et al

Poster (2021)

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 ... [more ▼]

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. [less ▲]

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Peer Reviewed
See detailLow-light image enhancement of permanently shadowed lunar regions with physics-based machine learning
Moseley, Ben; Bickel, Valentin; Lopez-Francos, Ignacio et al

in Low-light image enhancement of permanently shadowed lunar regions with physics-based machine learning (2020, December)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 221 (29 UL)