A two‐stage method for single image de‐raining based on attention smoothed dilated network
Shuangli Du, Hengrui Fan, Minghua Zhao, Haomai Zong, Jing Hu, Peng Li · IET Image Processing · 2022
Abstract 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.