  Insufficient image spatial resolution is a serious limitation in many
practical scenarios, especially when acquiring images at a finer scale is
infeasible or brings higher costs. This is inherent to remote sensing,
including Sentinel-2 satellite images that are available free of charge at a
high revisit frequency, but whose spatial resolution is limited to 10 m ground
sampling distance. The resolution can be increased with super-resolution
algorithms, in particular when performed from multiple images captured at
subsequent revisits of a satellite, taking advantage of information fusion that
leads to enhanced reconstruction accuracy. One of the obstacles in multi-image
super-resolution consists in the scarcity of real-world benchmarks - commonly,
simulated data are exploited which do not fully reflect the operating
conditions. In this paper, we introduce a new MuS2 benchmark for
super-resolving multiple Sentinel-2 images, with WorldView-2 imagery used as
the high-resolution reference. Within MuS2, we publish the first end-to-end
evaluation procedure for this problem which we expect to help the researchers
in advancing the state of the art in multi-image super-resolution.
