  Super-resolution suffers from an innate ill-posed problem that a single
low-resolution (LR) image can be from multiple high-resolution (HR) images.
Recent studies on the flow-based algorithm solve this ill-posedness by learning
the super-resolution space and predicting diverse HR outputs. Unfortunately,
the diversity of the super-resolution outputs is still unsatisfactory, and the
outputs from the flow-based model usually suffer from undesired artifacts which
causes low-quality outputs. In this paper, we propose FS-NCSR which produces
diverse and high-quality super-resolution outputs using frequency separation
and noise conditioning compared to the existing flow-based approaches. As the
sharpness and high-quality detail of the image rely on its high-frequency
information, FS-NCSR only estimates the high-frequency information of the
high-resolution outputs without redundant low-frequency components. Through
this, FS-NCSR significantly improves the diversity score without significant
image quality degradation compared to the NCSR, the winner of the previous
NTIRE 2021 challenge.
