  Recently data-driven image inpainting methods have made inspiring progress,
impacting fundamental image editing tasks such as object removal and damaged
image repairing. These methods are more effective than classic approaches,
however, due to memory limitations they can only handle low-resolution inputs,
typically smaller than 1K. Meanwhile, the resolution of photos captured with
mobile devices increases up to 8K. Naive up-sampling of the low-resolution
inpainted result can merely yield a large yet blurry result. Whereas, adding a
high-frequency residual image onto the large blurry image can generate a sharp
result, rich in details and textures. Motivated by this, we propose a
Contextual Residual Aggregation (CRA) mechanism that can produce high-frequency
residuals for missing contents by weighted aggregating residuals from
contextual patches, thus only requiring a low-resolution prediction from the
network. Since convolutional layers of the neural network only need to operate
on low-resolution inputs and outputs, the cost of memory and computing power is
thus well suppressed. Moreover, the need for high-resolution training datasets
is alleviated. In our experiments, we train the proposed model on small images
with resolutions 512x512 and perform inference on high-resolution images,
achieving compelling inpainting quality. Our model can inpaint images as large
as 8K with considerable hole sizes, which is intractable with previous
learning-based approaches. We further elaborate on the light-weight design of
the network architecture, achieving real-time performance on 2K images on a GTX
1080 Ti GPU. Codes are available at: Atlas200dk/sample-imageinpainting-HiFill.
