Despite efforts to construct super-resolution (SR) training datasets with a wide range of degradation scenarios, existing supervised methods based on these datasets still struggle to consistently offer promising results due to the diversity of real-world degradation scenarios and the inherent complexity of model learning. Our work explores a new route: integrating the sample-adaptive property learned through image intrinsic self-similarity and the universal knowledge acquired from large-scale data. We achieve this by uniting internal learning and external learning by an unrolled optimization process. With the merits of both, the tuned fully-supervised SR models can be augmented to broadly handle the real-world degradation in a plug-and-play style. Furthermore, to promote the efficiency of combining internal/external learning, we apply an attention-based weight-updating method to guide the mining of self-similarity, and various data augmentations are adopted while applying the exponential moving average strategy. We conduct extensive experiments on real-world degraded images and our approach outperforms other methods in both qualitative and quantitative comparisons. Our project is available at: https://github.com/ZahraFan/AdaSSR/ .

中文翻译：

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通过自适应自相似性挖掘实现真实世界的超分辨率


尽管努力构建具有广泛退化情景的超分辨率 （SR） 训练数据集，但由于现实世界退化情景的多样性和模型学习的固有复杂性，基于这些数据集的现有监督方法仍然难以始终如一地提供有希望的结果。我们的工作探索了一条新路线：将通过图像内在自相似性学习的样本自适应特性与从大规模数据中获得的普遍知识相结合。我们通过展开的优化流程将内部学习和外部学习结合起来来实现这一目标。凭借这两者的优点，经过调整的完全监督 SR 模型可以进行扩展，以即插即用的方式广泛处理现实世界的退化。此外，为了提高内部/外部学习相结合的效率，我们采用基于注意力的权重更新方法来指导自相似性的挖掘，并在应用指数移动平均策略的同时采用了各种数据增强。我们对现实世界的退化图像进行了广泛的实验，我们的方法在定性和定量比较方面都优于其他方法。我们的项目可在以下网址获得： https://github.com/ZahraFan/AdaSSR/ .