Super-resolution (SR) techniques designed for real-world applications commonly encounter two primary challenges: generalization performance and restoration accuracy. We demonstrate that when methods are trained using complex, large-range degradations to enhance generalization, a decline in accuracy is inevitable. However, since the degradation in a certain real-world applications typically exhibits a limited variation range, it becomes feasible to strike a trade-off between generalization performance and testing accuracy within this scope. In this work, we introduce a novel approach to craft training degradation distributions using a small set of reference images. Our strategy is founded upon the binned representation of the degradation space and the Fr\'echet distance between degradation distributions. Our results indicate that the proposed technique significantly improves the performance of test images while preserving generalization capabilities in real-world applications.

翻译：超分辨率（SR）技术在实际应用中通常面临两大挑战：泛化性能与复原精度。我们证明，当采用复杂且大范围的退化进行训练以提升泛化能力时，精度的下降是不可避免的。然而，由于特定实际应用中的退化通常呈现有限的变异范围，在此范围内实现泛化性能与测试精度之间的权衡是可行的。本文提出一种新颖的方法，利用少量参考图像来设计训练退化分布。该策略基于退化空间的离散化表示以及退化分布之间的弗雷歇距离。结果表明，所提出的技术能显著提升测试图像的复原性能，同时保持实际应用中的泛化能力。