Image Inpainting Using Channel Attention and Hierarchical Residual Networks

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 and decoder respectively, which can improve the ability of the model to extract and express occluded image features. Finally, we designed a dilated multi-scale channel-attention block and inserted it into the skip-connection of the generator. This block can improve the utilization efficiency of low-level features in the encoder. Experimental results show that our model outperforms other classical inpainting approaches in the face, street-view inpainting tasks, both qualitatively and quantitatively.