  Most current super-resolution methods rely on low and high resolution image
pairs to train a network in a fully supervised manner. However, such image
pairs are not available in real-world applications. Instead of directly
addressing this problem, most works employ the popular bicubic downsampling
strategy to artificially generate a corresponding low resolution image.
Unfortunately, this strategy introduces significant artifacts, removing natural
sensor noise and other real-world characteristics. Super-resolution networks
trained on such bicubic images therefore struggle to generalize to natural
images. In this work, we propose an unsupervised approach for image
super-resolution. Given only unpaired data, we learn to invert the effects of
bicubic downsampling in order to restore the natural image characteristics
present in the data. This allows us to generate realistic image pairs,
faithfully reflecting the distribution of real-world images. Our
super-resolution network can therefore be trained with direct pixel-wise
supervision in the high resolution domain, while robustly generalizing to real
input. We demonstrate the effectiveness of our approach in quantitative and
qualitative experiments.
