  Satellite imaging generally presents a trade-off between the frequency of
acquisitions and the spatial resolution of the images. Super-resolution is
often advanced as a way to get the best of both worlds. In this work, we
investigate multi-image super-resolution of satellite image time series, i.e.
how multiple images of the same area acquired at different dates can help
reconstruct a higher resolution observation. In particular, we extend
state-of-the-art deep single and multi-image super-resolution algorithms, such
as SRDiff and HighRes-net, to deal with irregularly sampled Sentinel-2 time
series. We introduce BreizhSR, a new dataset for 4x super-resolution of
Sentinel-2 time series using very high-resolution SPOT-6 imagery of Brittany, a
French region. We show that using multiple images significantly improves
super-resolution performance, and that a well-designed temporal positional
encoding allows us to perform super-resolution for different times of the
series. In addition, we observe a trade-off between spectral fidelity and
perceptual quality of the reconstructed HR images, questioning future
directions for super-resolution of Earth Observation data.
