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Image Inpainting Using Channel Attention and Hierarchical Residual Networks
Journal of Computer-Aided Design & Computer Graphics
Existing deep-learning-based inpainting methods may have some shortcomings in perceiving and presenting image information at multi-scales. For this problem, we proposed an image inpainting model based on multi-scale channel attention and a hierarchical residual backbone network. Firstly, we adopted a U-Net architecture as the generator backbone of our inpainting model to encode and decode the damaged image. Secondly, we built multi-scale hierarchical residual structures in the encoder anddoi:10.3724/sp.j.1089.2021.18514 fatcat:jyvni5nq6nhwdmwrehcdd4xgyi