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

EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining