Single Image Deraining via Scale-space Invariant Attention Neural Network
This work improves image enhancement for outdoor visual computing systems, but it appears incremental as it builds on existing multi-scale and attention mechanisms.
The paper tackled the problem of removing rain artifacts from single images by addressing scale variations in rain streaks, resulting in superior performance over state-of-the-art methods on synthetic and real rainy scenes.
Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems. In this paper, we tackle the notion of scale that deals with visual changes in appearance of rain steaks with respect to the camera. Specifically, we revisit multi-scale representation by scale-space theory, and propose to represent the multi-scale correlation in convolutional feature domain, which is more compact and robust than that in pixel domain. Moreover, to improve the modeling ability of the network, we do not treat the extracted multi-scale features equally, but design a novel scale-space invariant attention mechanism to help the network focus on parts of the features. In this way, we summarize the most activated presence of feature maps as the salient features. Extensive experiments results on synthetic and real rainy scenes demonstrate the superior performance of our scheme over the state-of-the-arts.