Rain can severely hamper the visibility of scene objects. Although existing deep learning methods have reported promising performance, they often fail to obtain satisfactory results in many practical situations, especially when the input image contains both rain streaks and haze-like degradation. In this paper, a new two-stage method based on attention smoothed dilated network (SDN) is proposed. Unlike most fully-supervised methods, the mixture of rain streaks and haze-like effects is considered in the model. The proposed method consists of two stages. First, a generative adversarial network guided by the rain-streak attention map is proposed to remove rain streaks, where a multi-stage attention module is used to accurately locate rain streaks in the generator. Second, haze-like effects are further removed through SDN with the same structure as the generator. Extensive experiments on multiple datasets show that the method outperforms the state-of-the-art in both objective evaluation and visual quality.

Single image de-raining based on convolutional neural network (CNN) has made considerable progress in recent years. However, usually the de-rained result has dark artifacts and image textures tend to be over-smoothed. In this paper, a pyramid non-local enhanced residual dense network is proposed to reduce such distortion. Firstly, the down-sampled images are input into the Laplacian pyramid, which can extract the overall and partial texture clues, and subsequently a set of images of different scales are produced. Secondly, these images are fed into a non-local enhanced residual dense block, which can not only capture long-distance dependencies of feature maps, but also fully utilizes the hierarchical features in every dense block, leading to high accuracy of rain streaks extraction and better preservation of image edge detail. Finally, the de-rained image is gradually restored by Gaussian reconstruction pyramid. Experimental results on both synthetic data and realworld data show that the artifacts distortion is obviously reduced by the proposed network. And the quality of de-rained image is significantly improved compared with the state-ofthe-art methods.