  Image super-resolution (SR) is a representative low-level vision problem.
Although deep SR networks have achieved extraordinary success, we are still
unaware of their working mechanisms. Specifically, whether SR networks can
learn semantic information, or just perform complex mapping function? What
hinders SR networks from generalizing to real-world data? These questions not
only raise our curiosity, but also influence SR network development. In this
paper, we make the primary attempt to answer the above fundamental questions.
After comprehensively analyzing the feature representations (via dimensionality
reduction and visualization), we successfully discover the distinctive
"semantics" in SR networks, i.e., deep degradation representations (DDR), which
relate to image degradation instead of image content. We show that a
well-trained deep SR network is naturally a good descriptor of degradation
information. Our experiments also reveal two key factors (adversarial learning
and global residual) that influence the extraction of such semantics. We
further apply DDR in several interesting applications (such as distortion
identification, blind SR and generalization evaluation) and achieve promising
results, demonstrating the correctness and effectiveness of our findings.
