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.

探索和建模降雨生成机制对于增加配对数据减轻降雨图像处理模型的训练任务至关重要，该研究提出了一种新颖的基于深度学习的降雨生成器，充分考虑了降雨背后的物理生成机制，并将基本降雨因素（即形状、方向、长度、宽度和稀疏度）的学习明确地编码到深度网络中。我们首次通过合理采用滤波器参数化技术实现了一个在降雨因素上可精细控制且能够纯粹通过数据学习这些因素的深度网络。我们的生成实验表明，由提出的降雨生成器生成的降雨不仅质量更高，而且对于去雨和下游任务比目前最先进的降雨生成方法更有效。此外，我们的配对数据增强实验，包括分布内和分布外（OOD），进一步验证了我们模型生成的样本的多样性，用于分布内去雨和 OOD 泛化任务。