An Improved Generative Adversarial Network for Image Inpainting
An image inpainting model based on generative adversarial network is proposed to solve the problems of poor inpainting effect such as blurring and boundary protrusion after image restoration.First,the generator part of the Context Encoder(CE)model is modified using the UNet structure;second,multi-branch residual modules are used in the generator downsampling modules to improve the feature ex-traction capability of the model;finally,global consistency loss and TV loss are introduced in the loss function to combine the adversarial loss and local loss,thus improving the image inpainting.The generator network and discriminator network are trained alternately and adversarially until a stable generator model with better generation effect is derived to complete the image inpainting.The inpainting model was tested on the CelebA dataset,and the results showed a better inpainting effect with significant improvement in both SSIM and PSNR metrics.
NETL
NSTL
万方数据
维普
