Structure First Detail Next: Image Inpainting with Pyramid Generator

• URL: http://arxiv.org/abs/2106.08905v1
• Date: Wed, 16 Jun 2021 16:00:16 GMT
• Title: Structure First Detail Next: Image Inpainting with Pyramid Generator
• Authors: Shuyi Qu, Zhenxing Niu, Kaizhu Huang, Jianke Zhu, Matan Protter, Gadi Zimerman, Yinghui Xu
• Abstract summary: We propose to build a Pyramid Generator by stacking several sub-generators. Lower-layer sub-generators focus on restoring image structures while the higher-layer sub-generators emphasize image details. Our approach has a learning scheme of progressively increasing hole size, which allows it to restore large-hole images.
• Score: 26.94101909283021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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