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.

该论文提出了一种针对超分辨率图像的无监督方法，通过学习将人工生成的低分辨率图像的影响反演，从而还原数据中存在的自然图像特征，旨在解决使用人工生成的低分辨率图像所出现的问题。实验结果表明，该方法可以得到具有代表性的真实世界图像组，有效地提高超分辨率网络的泛化性。