  Deraining is a significant and fundamental computer vision task, aiming to
remove the rain streaks and accumulations in an image or video captured under a
rainy day. Existing deraining methods usually make heuristic assumptions of the
rain model, which compels them to employ complex optimization or iterative
refinement for high recovery quality. This, however, leads to time-consuming
methods and affects the effectiveness for addressing rain patterns deviated
from from the assumptions. In this paper, we propose a simple yet efficient
deraining method by formulating deraining as a predictive filtering problem
without complex rain model assumptions. Specifically, we identify
spatially-variant predictive filtering (SPFilt) that adaptively predicts proper
kernels via a deep network to filter different individual pixels. Since the
filtering can be implemented via well-accelerated convolution, our method can
be significantly efficient. We further propose the EfDeRain+ that contains
three main contributions to address residual rain traces, multi-scale, and
diverse rain patterns without harming the efficiency. First, we propose the
uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify
the difficulties of reconstructing clean pixels via predicted kernels and
remove the residual rain traces effectively. Second, we design the
weight-sharing multi-scale dilated filtering (WS-MS-DFilt) to handle
multi-scale rain streaks without harming the efficiency. Third, to eliminate
the gap across diverse rain patterns, we propose a novel data augmentation
method (i.e., RainMix) to train our deep models. By combining all contributions
with sophisticated analysis on different variants, our final method outperforms
baseline methods on four single-image deraining datasets and one video
deraining dataset in terms of both recovery quality and speed.
