Dianxin kexue (Aug 2024)
Lightweight face image restoration algorithm based on multi-scale feature fusion
Abstract
Aiming at the problems of poor quality of restored images and large number of model parameters in the current occluded face image restoration, a lightweight face image restoration model based on multi-scale feature fusion with improved U-Net, LM-UNET, was proposed. Firstly, the original convolution was replaced by a depthwise separable convolution to enhance the feature expression ability of the model for different channels and contextual information. Secondly, a multi-scale feature attention fusion module was designed in the jump connection to fully fuse the information of different scale features, and the embedded residual block reduced the semantic gap between features to improve the repair accuracy of the model. Finally, a positional attention module was introduced to enhance the salient information of the face image, and improve the model’s effective extraction ability of facial positional pixel information of the model. The algorithm was trained, validated and tested on the occluded face dataset MFD generated based on the CK+ dataset, and the PSNR of the repaired image reached 30.49 dB and SSIM reached 96.85%. The experimental results of comparing the model with the other models show that the model has better image quality and visual effect for restoration of the face in the presence of occlusion.
