TSFC: TEXTURE AND STRUCTURE FEATURES COUPLING FOR IMAGE INPAINTING

Image inpainting has made significant progress benefiting from the advantages of convolutional neural networks (CNNs). Deep learning-based methods have shown extraordinary performance in this field. In this paper, we propose a novel image inpainting architecture with pure CNN that can jointly reconstruct the structure and texture of the image. Our generative network architecture (TSFC) consists of two parallel stages: structure generation and texture generation. In the structure generation stage, we use the large convolution kernel, which is highly neglected in modern networks, using the effective perceptual field of the large convolution kernel to enhance the perception of overall structural features. In the texture generation stage, we use the small convolution kernel to extract local texture features. Qualitative and quantitative experimental results on CelebA-HQ and Paris Street View datasets demonstrate the effectiveness and superiority of our method.