Parallel Edge-Image Learning for Image Inpainting

Junfeng Hu, Chengxin Wang, Ying Zhang, Li Liu, Yifang Yin, Roger Zimmermann

Abstract: The primary goal of image inpainting is to fix holes in a damaged image with natural contents. A key challenge is that a damaged image contains complex structures in differ-ent ways, with each consisting of its configuration of edges and spatial dependencies. As a result, filled images often converge to unnatural and implausible results. Currently, the edge-image inpainting methods adopt two stages to recover edges and images successively, which suffer from feature in-consistency and error accumulation. This leads us to present a parallel edge-image learning framework that explicitly char-acterizes these internal configurations in a single stage. The framework introduces a dual parallel network-based decoder to generate the image and the edges concurrently, leading to feature consistency at the semantic level. Also, a new cross-fire mechanism aims to exchange edge-image information in the decoder, avoiding error accumulation. Empirical evaluations on benchmark datasets suggest that our approach out-performs the state-of-the-art methods on image inpainting.