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.