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

Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution