  In this work, we study the problem of single-image super-resolution (SISR) of
Sentinel-2 imagery. We show that thanks to its unique sensor specification,
namely the inter-band shift and alias, that deep-learning methods are able to
recover fine details. By training a model using a simple $L_1$ loss, results
are free of hallucinated details. For this study, we build a dataset of pairs
of images Sentinel-2/PlanetScope to train and evaluate our super-resolution
(SR) model.
