Multi-channel Image Inpainting Algorithm Based on Edge Prediction

This paper considers how to restore an image with large stained areas. An end to end model is proposed, which contains two networks, an edge generation adversarial network and a content generative adversarial network. First, the edge generation adversarial network is deployed to infer the missing boundaries of the image. Then the second network with a designed edge information channel is employed to restore the missing or stained areas of the image with the guidance of the inferred boundaries. Experiments were performed on ImageNet. The results show that the proposed model can better understand the semantic information of the stained area by introducing additional object contour channels and greatly improve the inpainting capability of the model. Quantitative evaluation indexes show that the proposed model is 4.5% better than the DeepFill V2 model in structural similarity and 7.1% better than the DeepFill V2 model in Peak Signal-to-Noise Ratio.