Area of image inpainting over relatively large missing regions recently
advanced substantially through adaptation of dedicated deep neural networks.
However, current network solutions still introduce undesired artifacts and
noise to the repaired regions. We present an image inpainting method that is
based on the celebrated generative adversarial network (GAN) framework. The
proposed PGGAN method includes a discriminator network that combines a global
GAN (G-GAN) architecture with a patchGAN approach. PGGAN first shares network
layers between G-GAN and patchGAN, then splits paths to produce two adversarial
losses that feed the generator network in order to capture both local
continuity of image texture and pervasive global features in images. The
proposed framework is evaluated extensively, and the results including
comparison to recent state-of-the-art demonstrate that it achieves considerable
improvements on both visual and quantitative evaluations.

本文提出了一种基于生成对抗网络（GAN）框架的图像修复方法，该方法利用全局 GAN（G-GAN）架构和 PatchGAN 方法结合进行鉴别器网络的构建，可较好地实现图像贴图的局部连续性和全局特征。经大量评估发现，与最新的现有技术相比，该框架在视觉和数量评估方面均取得了显著的改进。