To address the problem that existing algorithms cannot effectively integrate local details and global structures, this study proposes a three-stage face image restoration algorithm that incorporates attention mechanisms to optimize local and global features. In the first stage, the position attention module (PAM) and focused linear attention (FLA) are introduced to enhance the extraction of local texture details and global contextual features of the image. In the second stage of optimization, the convolutional block attention module (CBAM) is incorporated with skip connections. The design strengthens feature focus through the differentiated weight assignment in both channel and spatial dimensions, while effectively achieving refined reconstruction of local regions by utilizing the detail-preservation strategy during the downsampling process. Finally, the third stage of integrating features is introduced to make the restored image more robust. The experimental results show that the proposed method achieves average improvements of 0.1214 dB in PSNR and 0.0022 in SSIM, along with an average reduction of 0.00065 in LPIPS on the CelebA-HQ dataset, significantly enhancing both the restoration quality and visual perception of images.