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Image Inpainting Based on Self-attention and Gated Convolution

Recently, significant progress has been made in the challenging task of image inpainting based on deep learning methods. These methods can produce visually sound image structure and texture but generally result in distorted or blurred surfaces. It is mainly due to the inefficiency of convolutional neural networks in replicating spatial location information from a distance. We propose a novel Image inpainting model, named IISAGC (Image Inpainting based on Self-Attention Gated Convolution), which combines the self-attention and Gated Convolution mechanisms. This method can combine novel image structures with explicit use of the surrounding image features and remote pixel relations as references to make better predictions during network training. The model is a two-stage generation network based on a U-Net structure, which can process missing images in any position. Qualitatively and quantitatively, compared datasets including faces (CelebA), textures (DTD), Google Street View, Paris Street View show that our proposed method can obtain high-quality visual inpainting results.