  How to design proper training pairs is critical for super-resolving
real-world low-quality (LQ) images, which suffers from the difficulties in
either acquiring paired ground-truth high-quality (HQ) images or synthesizing
photo-realistic degraded LQ observations. Recent works mainly focus on modeling
the degradation with handcrafted or estimated degradation parameters, which are
however incapable to model complicated real-world degradation types, resulting
in limited quality improvement. Notably, LQ face images, which may have the
same degradation process as natural images, can be robustly restored with
photo-realistic textures by exploiting their strong structural priors. This
motivates us to use the real-world LQ face images and their restored HQ
counterparts to model the complex real-world degradation (namely ReDegNet), and
then transfer it to HQ natural images to synthesize their realistic LQ
counterparts. By taking these paired HQ-LQ face images as inputs to explicitly
predict the degradation-aware and content-independent representations, we could
control the degraded image generation, and subsequently transfer these
degradation representations from face to natural images to synthesize the
degraded LQ natural images. Experiments show that our ReDegNet can well learn
the real degradation process from face images. The restoration network trained
with our synthetic pairs performs favorably against SOTAs. More importantly,
our method provides a new way to handle the real-world complex scenarios by
learning their degradation representations from the facial portions, which can
be used to significantly improve the quality of non-facial areas. The source
code is available at https://github.com/csxmli2016/ReDegNet.
