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.

翻译：传统基于合成数据集训练的人脸超分辨率（FSR）方法通常对真实世界人脸图像泛化能力较差。近期研究采用复杂退化模型或训练网络模拟真实退化过程，但由于生成的低分辨率图像与真实低分辨率图像之间仍存在域差异，此类方法的性能受到限制。此外，由于域间隙的存在，当同时利用合成数据和真实数据训练超分辨率模型时，目标域的语义特征信息可能受到影响。本研究提出一种面向真实世界人脸超分辨率的教师-学生模型，该模型充分考虑真实数据与合成数据之间的域间隙，并利用循环网络的中间输出逐步整合多样化边缘信息。大量实验证明，本方法在获取真实世界FSR高质量人脸图像方面超越了现有最优方法。