Single-shot Semantic Image Inpainting with Densely Connected Generative Networks

Semantic image inpainting - a task to speculate and fill in large missing areas of a natural image, has shown exciting progress with the introduction of generative adversarial networks (GANs). But due to lack of sufficient understanding of semantic and spatial context, existing methods easily generate blurred boundary and distorted structure, which are inconsistent with the surrounding area. In this paper, we propose a new end-to-end framework named Single-shot Densely Connected Generative Network (SSDCGN), which generates visually realistic and semantically distinct pixels for the missing content by a battery of symmetric encoder-decoder groups. To maximize semantic extraction and realize precise spatial context localization, we involve a deeper densely skip connection in our network. Extensive experiments on Paris StreetView and ImageNet datasets show the superiority of our method.