  Performance and generalization ability are two important aspects to evaluate
the deep learning models. However, research on the generalization ability of
Super-Resolution (SR) networks is currently absent. Assessing the
generalization ability of deep models not only helps us to understand their
intrinsic mechanisms, but also allows us to quantitatively measure their
applicability boundaries, which is important for unrestricted real-world
applications. To this end, we make the first attempt to propose a
Generalization Assessment Index for SR networks, namely SRGA. SRGA exploits the
statistical characteristics of the internal features of deep networks to
measure the generalization ability. Specially, it is a non-parametric and
non-learning metric. To better validate our method, we collect a patch-based
image evaluation set (PIES) that includes both synthetic and real-world images,
covering a wide range of degradations. With SRGA and PIES dataset, we benchmark
existing SR models on the generalization ability. This work provides insights
and tools for future research on model generalization in low-level vision.
