Abstract: Image inpainting methods based on deep learning have achieved great progress. At present, most of the image inpainting methods use the input mask to reconstruct the degraded areas of the image. Based on this observation, a two-stage blind image inpainting network was proposed, comprising a mask prediction network and an image inpainting network. The input of a mask was not required in the whole inpainting process. The mask prediction network could automatically detect the degraded area of the image and generate a mask according to the input image, and the image inpainting network could restore the missing part of the input image based on the prediction mask. In order to make better use of global context information, a context-gated residual block (CGRB) module was designed based on context-gated convolution to extract feature information. In addition, the spatial attention residual block (SARB) was proposed to model the relationship between pixels in the long-distance image, filtering some irrelevant details. A large number of experimental results on the CelebA-HQ, FFHQ, and PairsStreet datasets show that the improved algorithm is superior to other comparison methods and can generate convincing images. 

Key words: image inpainting, blind image inpainting, context-gated convolution, context-gated residual block, spatial attention residual block