  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.
