Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (SR) by accessing both the source and target data. Considering privacy policies or transmission restrictions of source data in practical scenarios, we propose a SOurce-free Domain Adaptation framework for image SR (SODA-SR) to address this issue, i.e., adapt a source-trained model to a target domain with only unlabeled target data. SODA-SR leverages the source-trained model to generate refined pseudo-labels for teacher-student learning. To better utilize pseudo-labels, we propose a novel wavelet-based augmentation method, named Wavelet Augmentation Transformer (WAT), which can be flexibly incorporated with existing networks, to implicitly produce useful augmented data. WAT learns low-frequency information of varying levels across diverse samples, which is aggregated efficiently via deformable attention. Furthermore, an uncertainty-aware self-training mechanism is proposed to improve the accuracy of pseudo-labels, with inaccurate predictions being rectified by uncertainty estimation. To acquire better SR results and avoid overfitting pseudo-labels, several regularization losses are proposed to constrain target LR and SR images in the frequency domain. Experiments show that without accessing source data, SODA-SR outperforms state-of-the-art UDA methods in both synthetic$\rightarrow$real and real$\rightarrow$real adaptation settings, and is not constrained by specific network architectures.

翻译：无监督域适应（UDA）通过同时访问源域和目标域数据，可有效解决真实图像超分辨率（SR）中的域间隙问题。考虑到实际场景中源数据的隐私策略或传输限制，我们提出了一种用于图像SR的无源域适应框架（SODA-SR），即仅利用无标签目标数据将源域训练模型适配至目标域。SODA-SR利用源域训练模型生成精炼伪标签，用于师生学习。为更好利用伪标签，我们提出一种新型小波增强方法——小波增强Transformer（WAT），可灵活集成至现有网络，隐式生成有用的增强数据。WAT通过学习不同样本间多层级低频信息，并利用可变形注意力高效聚合。此外，我们提出不确定性感知自训练机制提升伪标签准确性，通过不确定性估计修正错误预测。为获得更优SR结果并避免伪标签过拟合，我们提出多种正则化损失项，在频域约束目标低分辨率和高分辨率图像。实验表明，无需访问源数据，SODA-SR在合成→真实和真实→真实适配场景中均优于最先进UDA方法，且不受特定网络架构限制。