Variational Image Deraining

Ying-Jun Du, Jun Xu, Qiang Qiu, Xiantong Zhen, Lei Zhang

Abstract: Images captured in severe weather such as rain and snow significantly degrade the accuracy of vision systems, e.g., for outdoor video surveillance or autonomous driving. Image deraining is a critical yet highly challenging task, due to the fact that rain density varies across spatial locations, while the distribution patterns simultaneously vary across color channels. In this paper, we propose a variational image deraining (VID) method by formulating image deraining in a conditional variational auto-encoder framework. To achieve adaptive deraining to spatial rain density, we generate a density estimation map for each color channel, which can largely avoid over and under deraining. In addition, to address cross-channel variations, we conduct channel-wise deraining, motivated by our observation that bright pixels do not tend to remain bright after deraining unless their color channels are handled separately. Experimental results show that the proposed deraining method achieves superior performance on both synthesized and real rainy images, surpassing previous state-of-the-art methods by large margins.