  For the single image rain removal (SIRR) task, the performance of deep
learning (DL)-based methods is mainly affected by the designed deraining models
and training datasets. Most of current state-of-the-art focus on constructing
powerful deep models to obtain better deraining results. In this paper, to
further improve the deraining performance, we novelly attempt to handle the
SIRR task from the perspective of training datasets by exploring a more
efficient way to synthesize rainy images. Specifically, we build a full
Bayesian generative model for rainy image where the rain layer is parameterized
as a generator with the input as some latent variables representing the
physical structural rain factors, e.g., direction, scale, and thickness. To
solve this model, we employ the variational inference framework to approximate
the expected statistical distribution of rainy image in a data-driven manner.
With the learned generator, we can automatically and sufficiently generate
diverse and non-repetitive training pairs so as to efficiently enrich and
augment the existing benchmark datasets. User study qualitatively and
quantitatively evaluates the realism of generated rainy images. Comprehensive
experiments substantiate that the proposed model can faithfully extract the
complex rain distribution that not only helps significantly improve the
deraining performance of current deep single image derainers, but also largely
loosens the requirement of large training sample pre-collection for the SIRR
task.
