  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.
