  Single-image deraining is rather challenging due to the unknown rain model.
Existing methods often make specific assumptions of the rain model, which can
hardly cover many diverse circumstances in the real world, making them have to
employ complex optimization or progressive refinement. This, however,
significantly affects these methods' efficiency and effectiveness for many
efficiency-critical applications. To fill this gap, in this paper, we regard
the single-image deraining as a general image-enhancing problem and originally
propose a model-free deraining method, i.e., EfficientDeRain, which is able to
process a rainy image within 10~ms (i.e., around 6~ms on average), over 80
times faster than the state-of-the-art method (i.e., RCDNet), while achieving
similar de-rain effects. We first propose the novel pixel-wise dilation
filtering. In particular, a rainy image is filtered with the pixel-wise kernels
estimated from a kernel prediction network, by which suitable multi-scale
kernels for each pixel can be efficiently predicted. Then, to eliminate the gap
between synthetic and real data, we further propose an effective data
augmentation method (i.e., RainMix) that helps to train network for real rainy
image handling.We perform comprehensive evaluation on both synthetic and
real-world rainy datasets to demonstrate the effectiveness and efficiency of
our method. We release the model and code in
https://github.com/tsingqguo/efficientderain.git.
