Recent deep generative models have achieved promising performance in image inpainting. However, it is still very challenging for a neural network to generate realistic image details and textures, due to its inherent spectral bias. By our understanding of how artists work, we suggest to adopt a `structure first detail next' workflow for image inpainting. To this end, we propose to build a Pyramid Generator by stacking several sub-generators, where lower-layer sub-generators focus on restoring image structures while the higher-layer sub-generators emphasize image details. Given an input image, it will be gradually restored by going through the entire pyramid in a bottom-up fashion. Particularly, our approach has a learning scheme of progressively increasing hole size, which allows it to restore large-hole images. In addition, our method could fully exploit the benefits of learning with high-resolution images, and hence is suitable for high-resolution image inpainting. Extensive experimental results on benchmark datasets have validated the effectiveness of our approach compared with state-of-the-art methods.
@article{arxiv.2106.08905,
title = {Structure First Detail Next: Image Inpainting with Pyramid Generator},
author = {Shuyi Qu and Zhenxing Niu and Kaizhu Huang and Jianke Zhu and Matan Protter and Gadi Zimerman and Yinghui Xu},
journal= {arXiv preprint arXiv:2106.08905},
year = {2021}
}