  Learning super-resolution (SR) network without the paired low resolution (LR)
and high resolution (HR) image is difficult because direct supervision through
the corresponding HR counterpart is unavailable. Recently, many real-world SR
researches take advantage of the unpaired image-to-image translation technique.
That is, they used two or more generative adversarial networks (GANs), each of
which translates images from one domain to another domain, \eg, translates
images from the HR domain to the LR domain. However, it is not easy to stably
learn such a translation with GANs using unpaired data. In this study, we
present a simple and efficient method of training of real-world SR network. To
stably train the network, we use statistics of an image patch, such as means
and variances. Our real-world SR framework consists of two GANs, one for
translating HR images to LR images (degradation task) and the other for
translating LR to HR (SR task). We argue that the unpaired image translation
using GANs can be learned efficiently with our proposed data sampling strategy,
namely, variance matching. We test our method on the NTIRE 2020 real-world SR
dataset. Our method outperforms the current state-of-the-art method in terms of
the SSIM metric as well as produces comparable results on the LPIPS metric.
