Cross-Image Context for Single Image Inpainting

Visual context is of crucial importance for image inpainting. The contextual information captures the appearance and semantic correlation between the image regions, helping to propagate the information of the complete regions for reasoning the content of the corrupted regions. Many inpainting methods compute the visual context based on the regions within a single image. In this paper, we propose the Cross-Image Context Memory (CICM) for learning and using the cross-image context to recover the corrupted regions. CICM consists of multiple sets of the cross-image features learned from the image regions with different visual patterns. The regional features are learned across different images, thus providing richer context that benefits the inpainting task. The experimental results demonstrate the effectiveness and generalization of CICM, which achieves state-of-the-art performances on various datasets for single image inpainting.