  Generative deep learning has sparked a new wave of Super-Resolution (SR)
algorithms that enhance single images with impressive aesthetic results, albeit
with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more
grounded approach to the ill-posed problem, by conditioning on multiple
low-resolution views. This is important for satellite monitoring of human
impact on the planet -- from deforestation, to human rights violations -- that
depend on reliable imagery. To this end, we present HighRes-net, the first deep
learning approach to MFSR that learns its sub-tasks in an end-to-end fashion:
(i) co-registration, (ii) fusion, (iii) up-sampling, and (iv)
registration-at-the-loss. Co-registration of low-resolution views is learned
implicitly through a reference-frame channel, with no explicit registration
mechanism. We learn a global fusion operator that is applied recursively on an
arbitrary number of low-resolution pairs. We introduce a registered loss, by
learning to align the SR output to a ground-truth through ShiftNet. We show
that by learning deep representations of multiple views, we can super-resolve
low-resolution signals and enhance Earth Observation data at scale. Our
approach recently topped the European Space Agency's MFSR competition on
real-world satellite imagery.
