  Recently, deep convolution neural networks (CNNs) steered face
super-resolution methods have achieved great progress in restoring degraded
facial details by jointly training with facial priors. However, these methods
have some obvious limitations. On the one hand, multi-task joint learning
requires additional marking on the dataset, and the introduced prior network
will significantly increase the computational cost of the model. On the other
hand, the limited receptive field of CNN will reduce the fidelity and
naturalness of the reconstructed facial images, resulting in suboptimal
reconstructed images. In this work, we propose an efficient CNN-Transformer
Cooperation Network (CTCNet) for face super-resolution tasks, which uses the
multi-scale connected encoder-decoder architecture as the backbone.
Specifically, we first devise a novel Local-Global Feature Cooperation Module
(LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a
Transformer block, to promote the consistency of local facial detail and global
facial structure restoration simultaneously. Then, we design an efficient
Feature Refinement Module (FRM) to enhance the encoded features. Finally, to
further improve the restoration of fine facial details, we present a
Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from
different stages in the encoder procedure. Extensive evaluations on various
datasets have assessed that the proposed CTCNet can outperform other
state-of-the-art methods significantly. Source code will be available at
https://github.com/IVIPLab/CTCNet.
