Structure First Detail Next: Image Inpainting with Pyramid Generator
This addresses the problem of high-resolution image inpainting with large holes for applications in computer vision, representing an incremental improvement over existing methods.
The paper tackles the challenge of generating realistic details and textures in image inpainting by proposing a 'structure first detail next' workflow using a Pyramid Generator, achieving state-of-the-art results on benchmark datasets.
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.