In the past few years, deep learning-based image inpainting has made significant progress. However, many existing methods do not take into account the rationality of the structure and the fineness of the texture, which leads to the scattered structure or excessive smoothness of the repaired image. To solve this problem, we propose a two-stage image inpainting model composed of structure generation network and texture generation network. The structure generation network focuses on the structure and color domain and uses the damaged structure map extracted from the mask image to reasonably fill the mask area to generate a complete structure map. The texture generation network uses the repaired structure map to guide the refinement process. We train the two-stage network on the public datasets Places2, CelebA, and Paris StreetView, and the experimental results show the superiority of our method over the previous methods.

中文翻译：

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基于深度学习的结构图图像修复

在过去的几年中，基于深度学习的图像修复取得了重大进展。然而，现有的许多方法没有考虑到结构的合理性和纹理的精细度，导致修复后的图像结构分散或过于平滑。为了解决这个问题，我们提出了一种由结构生成网络和纹理生成网络组成的两阶段图像修复模型。结构生成网络侧重于结构和色域，利用从掩膜图像中提取的受损结构图合理填充掩膜区域，生成完整的结构图。纹理生成网络使用修复后的结构图来指导细化过程。我们在公共数据集 Places2、CelebA 和 Paris StreetView 上训练两阶段网络，