Single image rain removal with reusing original input squeeze‐and‐excitation network
Meihua Wang, Lunbao Chen, Yun Liang, Yuexing Hao, Haijun He, Chao Li · IET Image Processing · 2020
In this study, the authors propose a novel network architecture to address the problem of removing rain streaks from single images. To strengthen the representational power of the network, they adopt the squeeze‐and‐excitation block in the network. Furthermore, they propose a new network connection called reusing original input (ROI). The ROI connection reuses the original input of the network and can provide more texture details of the background. These details can be useful for the restoration of the image after removing the rain streaks. Batch normalisation is applied to further improve the rain removal performance of the network. Despite the fact that the network is trained on synthetic data, experimental results show that the proposed network has a comparable performance on both synthetic images and real‐world images to the state‐of‐the‐art methods.