Image Completion Based on Edge Prediction and Improved Generator

The existing image completion algorithms may result in problems of poor completion in the missing parts, excessive smoothing or chaotic structure of the completed areas, and large training cycle when processing more complex images. Therefore, a two-stage adversarial image completion model based on edge prediction and improved generator structure has been put forward to solve the existing problems. Firstly, Canny edge detection is utilized to extract the damaged edge image, to predict and to complete the edge information of the missing area of the image in the edge prediction network. Secondly, the predicted edge image is taken as a priori information by the Image completion network to complete the damaged area of the image, so as to make the structure information of the completed area more accurate. A-JPU module is designed to ensure the completion result and speed up training for existing models due to the enormous number of computations caused by the large use of extended convolution in the self-coding structure. Finally, the experimental results on Places 2 dataset show that the PSNR and SSIM of the image using the image completion model are higher and the subjective visual effect is closer to the real image than some other image completion models.