DARGS: Image inpainting algorithm via deep attention residuals group and semantics

Abstract

To solving the problems that the existing image inpainting methods lack authenticity, do not deal with the information of missing and non-missing regions flexibly, and do not deal with the image feature information on different stages effectively, we propose an image restoration method combining Semantic Priors and Deep Attention Residual Group. The image restoration method mainly consists of Semantic Priors Network, Deep Attention Residual Group, and Full-scale Skip Connection. The Semantic Priors Network learns the complete semantic prior information of the visual elements in the missing regions and completes the missing regions with the learned semantic information. The deep attention residual set allows the generator to focus more on not only the missing regions of the image but also to learn the features of each channel adaptive. The full-scale jump joins can combine the low-level feature maps containing image boundaries with the high-level feature maps containing image textures and details to repair the missing regions. In this paper, a full comparison experiment is conducted on the CelebA-HQ and the Paris StreetView datasets, and the experimental results show that proposed method outperforms the current representative state-of-the-art image restoration methods.