Repeat multiview panchromatic super-resolution restoration using the UCL MAGiGAN system

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