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Image enhancement from degradation of rainy artifacts plays a critical rolein outdoor visual computing systems. In this paper, we tackle the notion ofscale that deals with visual changes in appearance of rain steaks with respectto the camera. Specifically, we revisit multi-scale representation byscale-space theory, and propose to represent the multi-scale correlation inconvolutional feature domain, which is more compact and robust than that inpixel domain. Moreover, to improve the modeling ability of the network, we donot treat the extracted multi-scale features equally, but design a novelscale-space invariant attention mechanism to help the network focus on parts ofthe features. In this way, we summarize the most activated presence of featuremaps as the salient features. Extensive experiments results on synthetic andreal rainy scenes demonstrate the superior performance of our scheme over thestate-of-the-arts.

Single Image Deraining via Scale-space Invariant Attention Neural Network