LAUSR

Deep learning is a hot method in face super-resolution reconstruction in recent years, but it needs to further improve the details of reconstructed images and speed up network training. This paper improves the deep residual network from two aspects of residual unit and network structure and proposes a face super-resolution reconstruction algorithm based on the improved model. We improve the network structure of the residual unit by connecting with a densely connected convolutional layer and removing the BN layer, thereby enhancing the information flow between the inner convolutional layers and eliminate the damage to the spatial information of the image by batch normalization processing. At the same time, we combine the output characteristics of each residual unit on the basis of the global residual structure, so the face feature information is more fully utilized and the model detail recovery ability is also improved. Experiments on FDDB and AFLW face datasets show that the proposed method has better performance in feature description and detail information reconstruction, and higher PSNR and SSIM than other methods.