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.

翻译：探索和建模降雨生成机制对于扩充配对数据以简化雨天图像处理模型的训练至关重要。针对这一任务，本研究提出了一种新颖的基于深度学习的降雨生成器，它充分考虑了下雨的物理生成机制，并将基本降雨因子（即形状、方向、长度、宽度和稀疏度）的学习明确嵌入深度网络。其重要意义在于，该生成器不仅像传统人工策略一样精心设计降雨的基本要素以模拟预期的雨，还能像深度学习方法一样精细地适应复杂多样的实际雨天图像。通过合理采用滤波器参数化技术，我们首次实现了一个能够精细控制降雨因子并仅从数据中学习这些因子分布的深度网络。我们的非配对生成实验表明，与当前最先进的降雨生成方法相比，所提出的降雨生成器生成的雨不仅质量更高，而且在去雨及下游任务中更为有效。此外，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在分布内去雨和OOD泛化任务中的多样性。