  Exploring and modeling rain generation mechanism is critical for augmenting
paired data to ease training of rainy image processing models. Against this
task, this study proposes a novel deep learning based rain generator, which
fully takes the physical generation mechanism underlying rains into
consideration and well encodes the learning of the fundamental rain factors
(i.e., shape, orientation, length, width and sparsity) explicitly into the deep
network. Its significance lies in that the generator not only elaborately
design essential elements of the rain to simulate expected rains, like
conventional artificial strategies, but also finely adapt to complicated and
diverse practical rainy images, like deep learning methods. By rationally
adopting filter parameterization technique, we first time achieve a deep
network that is finely controllable with respect to rain factors and able to
learn the distribution of these factors purely from data. Our unpaired
generation experiments demonstrate that the rain generated by the proposed rain
generator is not only of higher quality, but also more effective for deraining
and downstream tasks compared to current state-of-the-art rain generation
methods. Besides, the paired data augmentation experiments, including both
in-distribution and out-of-distribution (OOD), further validate the diversity
of samples generated by our model for in-distribution deraining and OOD
generalization tasks.
