The network architecture of our MPAN.

Previous general super-resolution methods do not perform well in restoring the details structure information of face images. Prior and attribute-based face super-resolution methods have improved performance with extra trained results. However, they need an additional network and extra training data are challenging to obtain. To address these issues, we propose a Multi-phase Attention Network (MPAN). Specifically, our proposed MPAN builds on integrated residual attention groups (IRAG) and a concatenated attention module (CAM). The IRAG consists of residual channel attention blocks (RCAB) and an integrated attention module (IAM). Meanwhile, we use IRAG to bootstrap the face structures. We utilize the CAM to concentrate on informative layers, hence improving the network’s ability to reconstruct facial texture features. We use the IAM to focus on important positions and channels, which makes the network more effective at restoring key face structures like eyes and mouths. The above two attention modules form the multi-phase attention mechanism. Extensive experiments show that our MPAN has a significant competitive advantage over other state-of-the-art networks on various scale factors using various metrics, including PSNR and SSIM. Overall, our proposed Multi-phase Attention mechanism significantly improves the network for recovering face HR images without using additional information.