While deep learning (DL)-based video deraining methods have achieved
significant success recently, they still exist two major drawbacks. Firstly,
most of them do not sufficiently model the characteristics of rain layers of
rainy videos. In fact, the rain layers exhibit strong physical properties
(e.g., direction, scale and thickness) in spatial dimension and natural
continuities in temporal dimension, and thus can be generally modelled by the
spatial-temporal process in statistics. Secondly, current DL-based methods
seriously depend on the labeled synthetic training data, whose rain types are
always deviated from those in unlabeled real data. Such gap between synthetic
and real data sets leads to poor performance when applying them in real
scenarios. Against these issues, this paper proposes a new semi-supervised
video deraining method, in which a dynamic rain generator is employed to fit
the rain layer, expecting to better depict its insightful characteristics.
Specifically, such dynamic generator consists of one emission model and one
transition model to simultaneously encode the spatially physical structure and
temporally continuous changes of rain streaks, respectively, which both are
parameterized as deep neural networks (DNNs). Further more, different prior
formats are designed for the labeled synthetic and unlabeled real data, so as
to fully exploit the common knowledge underlying them. Last but not least, we
also design a Monte Carlo EM algorithm to solve this model. Extensive
experiments are conducted to verify the superiorities of the proposed
semi-supervised deraining model.

本文提出了一种新的半监督视频去雨方法，其中采用动态雨生成器来适应雨层，以更好地描绘其深刻的特征。该方法具有良好的性能，并可同时编码雨渍的物理结构和时间连续性变化，通过设计不同的先验格式，充分利用标记合成和未标记真实数据之间的共同知识，使用蒙特卡罗 EM 算法进行求解。