The Family of Onion Convolutions for Image Inpainting

Recently deep learning methods have achieved great success in image inpainting problem. However, reconstructing continuities of complex structures with non-stationary textures remains a challenging task for computer vision. In this paper the family of onion convolutions is presented, the concept of which arises from a connection between patch-based techniques and attention mechanisms . The onion convolutions are building blocks designed for the iterative completion of the missing region from its boundary to the center. It allows to continuously propagate structures and textures from the known region to the missing one and meet human criteria on high-quality image completions. As qualitative and quantitative comparisons show, our method with onion convolutions outperforms state-of-the-art methods by producing more realistic, visually plausible and semantically coherent results.