Abstract
This paper introduces a semi-parametric approach to image inpainting for irregular holes. The nonparametric part consists of an external image database. During test time database is used to retrieve a supplementary image, similar to the input masked picture, and utilize it as auxiliary information for the deep neural network. Further, we propose a novel method of generating masks with irregular holes and present public dataset with such masks. Experiments on CelebA-HQ dataset show that our semi-parametric method yields more realistic results than previous approaches, which is confirmed by the user study.
Abstract (translated)
本文介绍了一种用于不规则孔洞的图像修复的半参数方法。非参数部分由外部图像数据库组成。在测试时间期间，数据库用于检索补充图像，类似于输入屏蔽图像，并将其用作深度神经网络的辅助信息。此外，我们提出了一种生成具有不规则孔的掩模的新方法，并且提供了具有这种掩模的公共数据集。 CelebA-HQ数据集上的实验表明，我们的半参数方法比以前的方法产生更真实的结果，用户研究证实了这一点。
URL
https://arxiv.org/abs/1807.02855
