  Many super-resolution (SR) models are optimized for high performance only and
therefore lack efficiency due to large model complexity. As large models are
often not practical in real-world applications, we investigate and propose
novel loss functions, to enable SR with high perceptual quality from much more
efficient models. The representative power for a given low-complexity generator
network can only be fully leveraged by strong guidance towards the optimal set
of parameters. We show that it is possible to improve the performance of a
recently introduced efficient generator architecture solely with the
application of our proposed loss functions. In particular, we use a Fourier
space supervision loss for improved restoration of missing high-frequency (HF)
content from the ground truth image and design a discriminator architecture
working directly in the Fourier domain to better match the target HF
distribution. We show that our losses' direct emphasis on the frequencies in
Fourier-space significantly boosts the perceptual image quality, while at the
same time retaining high restoration quality in comparison to previously
proposed loss functions for this task. The performance is further improved by
utilizing a combination of spatial and frequency domain losses, as both
representations provide complementary information during training. On top of
that, the trained generator achieves comparable results with and is 2.4x and
48x faster than state-of-the-art perceptual SR methods RankSRGAN and SRFlow
respectively.
