  Recently image inpainting has witnessed rapid progress due to generative
adversarial networks (GAN) that are able to synthesize realistic contents.
However, most existing GAN-based methods for semantic inpainting apply an
auto-encoder architecture with a fully connected layer, which cannot accurately
maintain spatial information. In addition, the discriminator in existing GANs
struggle to understand high-level semantics within the image context and yield
semantically consistent content. Existing evaluation criteria are biased
towards blurry results and cannot well characterize edge preservation and
visual authenticity in the inpainting results. In this paper, we propose an
improved generative adversarial network to overcome the aforementioned
limitations. Our proposed GAN-based framework consists of a fully convolutional
design for the generator which helps to better preserve spatial structures and
a joint loss function with a revised perceptual loss to capture high-level
semantics in the context. Furthermore, we also introduce two novel measures to
better assess the quality of image inpainting results. Experimental results
demonstrate that our method outperforms the state of the art under a wide range
of criteria.
