Image Inpainting Using Double Discriminator Generative Adversarial Networks

Abstract

The image inpainting algorithms based on GAN have problems such as color inconsistencies of the inpainted region with the surrounding part and the mode collapse during the training. This paper proposes an inpainting algorithm based on double discriminator generative adversarial networks (GANs). This algorithm introduces the loss function of WGAN-GP into a global discriminator and local discriminator independently. It combines the Mean-Square Error (MSE) and feature reconstruction loss function to train the inpainting model and repair the missing area. The algorithm uses more dilated convolutions instead of standard convolutions to obtain a larger receptive field and the skip connection to enhance the structural prediction ability of the generator. The experiment results on the labeled faces in the wild dataset show that our algorithm improves the quality of image inpainting and the stability of the training process.