Deep Two-Stage High-Resolution Image Inpainting
This addresses a bottleneck for interactive image processing tools by enabling high-resolution inpainting without artifacts, though it is incremental as it builds on existing models.
The paper tackles the problem of image inpainting at arbitrary resolutions, where existing deep methods fail due to artifacts when resolution increases, and proposes a method that makes models resolution-independent without retraining, achieving improved texture restoration.
In recent years, the field of image inpainting has developed rapidly, learning based approaches show impressive results in the task of filling missing parts in an image. But most deep methods are strongly tied to the resolution of the images on which they were trained. A slight resolution increase leads to serious artifacts and unsatisfactory filling quality. These methods are therefore unsuitable for interactive image processing. In this article, we propose a method that solves the problem of inpainting arbitrary-size images. We also describe a way to better restore texture fragments in the filled area. For this, we propose to use information from neighboring pixels by shifting the original image in four directions. Moreover, this approach can work with existing inpainting models, making them almost resolution independent without the need for retraining. We also created a GIMP plugin that implements our technique. The plugin, code, and model weights are available at https://github.com/a-mos/High_Resolution_Image_Inpainting.