  Image deraining is an important yet challenging image processing task. Though
deterministic image deraining methods are developed with encouraging
performance, they are infeasible to learn flexible representations for
probabilistic inference and diverse predictions. Besides, rain intensity varies
both in spatial locations and across color channels, making this task more
difficult. In this paper, we propose a Conditional Variational Image Deraining
(CVID) network for better deraining performance, leveraging the exclusive
generative ability of Conditional Variational Auto-Encoder (CVAE) on providing
diverse predictions for the rainy image. To perform spatially adaptive
deraining, we propose a spatial density estimation (SDE) module to estimate a
rain density map for each image. Since rain density varies across different
color channels, we also propose a channel-wise (CW) deraining scheme.
Experiments on synthesized and real-world datasets show that the proposed CVID
network achieves much better performance than previous deterministic methods on
image deraining. Extensive ablation studies validate the effectiveness of the
proposed SDE module and CW scheme in our CVID network. The code is available at
\url{https://github.com/Yingjun-Du/VID}.
