  Synthetic high-resolution (HR) \& low-resolution (LR) pairs are widely used
in existing super-resolution (SR) methods. To avoid the domain gap between
synthetic and test images, most previous methods try to adaptively learn the
synthesizing (degrading) process via a deterministic model. However, some
degradations in real scenarios are stochastic and cannot be determined by the
content of the image. These deterministic models may fail to model the random
factors and content-independent parts of degradations, which will limit the
performance of the following SR models. In this paper, we propose a
probabilistic degradation model (PDM), which studies the degradation
$\mathbf{D}$ as a random variable, and learns its distribution by modeling the
mapping from a priori random variable $\mathbf{z}$ to $\mathbf{D}$. Compared
with previous deterministic degradation models, PDM could model more diverse
degradations and generate HR-LR pairs that may better cover the various
degradations of test images, and thus prevent the SR model from over-fitting to
specific ones. Extensive experiments have demonstrated that our degradation
model can help the SR model achieve better performance on different datasets.
The source codes are released at \url{git@github.com:greatlog/UnpairedSR.git}.
