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Repeat multiview panchromatic super-resolution restoration using the UCL MAGiGAN system
Author(s): Y. Tao; J.-P. Muller
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Paper Abstract

High spatial resolution imaging data is always considered desirable in the field of remote sensing, particularly Earth observation. However, given the physical constraints of the imaging instruments themselves, one needs to be able to trade-off spatial resolution against launch mass as well as telecommunications bandwidth for transmitting data back to the Earth. In this paper, we present a newly developed super-resolution restoration system, called MAGiGAN, based on our original GPT-SRR system combined with deep learning image networks to be able to restore up to 4x higher resolution enhancement using multi-angle repeat images as input.

Paper Details

Date Published: 9 October 2018
PDF: 11 pages
Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 1078903 (9 October 2018); doi: 10.1117/12.2500196
Show Author Affiliations
Y. Tao, Univ. College London (United Kingdom)
J.-P. Muller, Univ. College London (United Kingdom)


Published in SPIE Proceedings Vol. 10789:
Image and Signal Processing for Remote Sensing XXIV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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