  Latest advances in Super-Resolution (SR) have been tested with general
purpose images such as faces, landscapes and objects, mainly unused for the
task of super-resolving Earth Observation (EO) images. In this research paper,
we benchmark state-of-the-art SR algorithms for distinct EO datasets using both
Full-Reference and No-Reference Image Quality Assessment (IQA) metrics. We also
propose a novel Quality Metric Regression Network (QMRNet) that is able to
predict quality (as a No-Reference metric) by training on any property of the
image (i.e. its resolution, its distortions...) and also able to optimize SR
algorithms for a specific metric objective. This work is part of the
implementation of the framework IQUAFLOW which has been developed for
evaluating image quality, detection and classification of objects as well as
image compression in EO use cases. We integrated our experimentation and tested
our QMRNet algorithm on predicting features like blur, sharpness, snr, rer and
ground sampling distance (GSD) and obtain validation medRs below 1.0 (out of
N=50) and recall rates above 95\%. Overall benchmark shows promising results
for LIIF, CAR and MSRN and also the potential use of QMRNet as Loss for
optimizing SR predictions. Due to its simplicity, QMRNet could also be used for
other use cases and image domains, as its architecture and data processing is
fully scalable.
