A Generate Adversarial Network with Structural Branch Assistance for Image Inpainting

In digital image inpainting tasks, existing deep-learning-based image inpainting methods have achieved remarkable staged results by introducing structural prior information into the network. However, the corresponding relationship between texture and structure is not fully considered, and the inconsistency between texture and structure appears in the results of the current method. In this paper, we propose a dual-branch network with structural branch assistance, which decouples the inpainting of low-frequency and high-frequency information utilizing parallel branches. The feature fusion (FF) module is introduced to integrate the feature information from the two branches, which effectively ensures the consistency of structure and texture in the image. The feature attention (FA) module is introduced to extract long-distance feature information, which enhances the consistency between the local features of the image and the overall image and gives the image a more detailed texture. Experiments on the Paris StreetView and CelebA-HQ datasets prove the effectiveness and superiority of our method.