  Traditional face super-resolution (FSR) methods trained on synthetic datasets
usually have poor generalization ability for real-world face images. Recent
work has utilized complex degradation models or training networks to simulate
the real degradation process, but this limits the performance of these methods
due to the domain differences that still exist between the generated
low-resolution images and the real low-resolution images. Moreover, because of
the existence of a domain gap, the semantic feature information of the target
domain may be affected when synthetic data and real data are utilized to train
super-resolution models simultaneously. In this study, a real-world face
super-resolution teacher-student model is proposed, which considers the domain
gap between real and synthetic data and progressively includes diverse edge
information by using the recurrent network's intermediate outputs. Extensive
experiments demonstrate that our proposed approach surpasses state-of-the-art
methods in obtaining high-quality face images for real-world FSR.
