TRFN: Triple-Receptive-Field Network for Regional-Texture and Holistic-Structure Image Inpainting

Qingguo Xiao, Zhiyuan Han, Zhaodong Liu, Guangyuan Pan, Yanpeng Zheng

Abstract: Image inpainting is a challenging task and has become a hot issue in recent years. Despite the significant progress of modern methods, it is still difficult to fill arbitrary missing regions with both vivid textures and coherent structures. Because of the limited receptive fields, methods centered on convolution neural networks only deal with regular textures but lose holistic structures. To this end, we propose a Triple-Receptive-Field Network (TRFN) that fuses local convolution features, global attention mechanism, and frequency domain learning in this study. TRFN roots in a concurrent structure that enables different receptive fields and retains local features and global representations to the maximum extent. TRFN captures effective representations and generates simultaneously detailed textures and holistic structures by using the concurrent structure. Experiments demonstrate the efficacy of TRFN and the proposed method achieves outstanding performance over the competitors.