Face Image Inpainting with Generative Adversarial Network - LIN Zhen-xian, ZHANG Meng-kai, WU Cheng-mao, ZHENG Xing-ning (Jisuanji kexue, 2021)
Face image inpainting is a hot topic of image processing research in recent years.Due to the loss of excessive sematic information,it is a difficult problem to inpaint large area missing of face images.Aiming at the problem of inpainting face images,a step-by-step image inpainting algorithm based on generative adversarial network is proposed.Face images inpainting task is divided into two steps.Firstly,face images are completed through the pre-completion network,and pre-completion images is enhanced feature through the enhancement network.The discriminator judges the difference between the pre-completion images,the enhanced images and the ideal image respectively.The long-term memory unit is used to connect the information flow of two parts.Secondly,the adversarial loss,content loss and total variation loss are combined to improve the effectively.Experiments are conducted on CelebA dataset,and this algorithm has an improvement of 16.84%~22.85% in PSNR and 10%～12.82% in SSIM compared with others typical image inpainting algorithms