Double Global and Local Information-based Image Inpainting

With the development of deep learning, significant progress has been made in image inpainting. Deep learning-based image inpainting methods can generate visually plausible inpainting results. However, the inpainting images may include the distortions or artifacts, especially at boundaries and high-texture regions. To address these issues, we propose an improved two-stage inpainting model with double local and global information. In the first stage, an Local Binary Pattern (LBP) learning network based on the U-Net architecture is employed to accurately predict the semantic and structural information of the missing regions. In the second stage, the double local and global network based on spatial attention module and Double-PatchGAN Discriminator (DPD) are proposed for further refinement. Aim to achieve the accurate, realistic, and high-quality inpainting results, the Multiple Loss Functions (MLF) is designed to strengthen the information at different levels. Extensive experiments conducted on public datasets, including CelebA-HQ, Places2 and Paris StreetView, demonstrate that our model outperforms several existing methods in terms of image inpainting.