  Data augmentation is an effective way to improve the performance of deep
networks. Unfortunately, current methods are mostly developed for high-level
vision tasks (e.g., classification) and few are studied for low-level vision
tasks (e.g., image restoration). In this paper, we provide a comprehensive
analysis of the existing augmentation methods applied to the super-resolution
task. We find that the methods discarding or manipulating the pixels or
features too much hamper the image restoration, where the spatial relationship
is very important. Based on our analyses, we propose CutBlur that cuts a
low-resolution patch and pastes it to the corresponding high-resolution image
region and vice versa. The key intuition of CutBlur is to enable a model to
learn not only "how" but also "where" to super-resolve an image. By doing so,
the model can understand "how much", instead of blindly learning to apply
super-resolution to every given pixel. Our method consistently and
significantly improves the performance across various scenarios, especially
when the model size is big and the data is collected under real-world
environments. We also show that our method improves other low-level vision
tasks, such as denoising and compression artifact removal.
