Structure-Aware Multi-Feature Co-Learning for Dual Branch Face Super Resolution

Recently, face super-resolution has achieved pleasing performance. Numerous works have shown that texture features and structural information play a crucial role for super-resolution reconstruction. However, effective co-learning of both has been limiting the performance improvement of existing state-of-the-art methods. Therefore, we focus on the texture and structure of images in this paper, and design a two-branch network containing a texture network (T-Net) and a structure network (S-Net) to jointly explore texture and structure information for co-learning. T-Net serves as the backbone network to learn both texture and structure information for reconstruction, while the S-Net serves as the auxiliary network that can effectively exploit the multi-scale information of the T-Net encoder to recover the structure. To better facilitate the co-learning of the two branches, two co-learning modules deal with the information flow interaction between the encoder and decoder of the two branches, respectively, thus explicitly guiding the structure-aware image reconstruction. Additionally, a dense feature enhancement module investigates the channel and spatial correlation of features and enhances the representation capability of the network. Extensive experiments on the CelebA and Helen datasets show that our proposed approach outperforms state-of-the-art methods.