Affiliations: [a]
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| [b]
College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an, China
Correspondence:
[*]
Corresponding author. Hongjuan Wang, College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an, Shandong Province, China. E-mail: [email protected].
Abstract: Single image rain removal remains a crucial and challenging low-level image processing task while significantly for outdoor-based high-level computer vision tasks. Recently, deep convolutional neural networks (CNNs) have become the mainstream structure of removing rain streaks and obtained remarkable performance. However, most of the existing CNN-based methods mainly pay attention to completely removing rain streaks while neglecting the restoration of details after deraining, which suffer from poor visual performance. In this paper, we propose a deep residual attention and encoder-decoder network to overcome the above shortcoming. Specifically, we develop an excellent basic block that contains dual parallel paths which are called rain removal network and detail restore network, respectively, to perform entirely and in-depth mapping relationships from rain to no-rain. The upper rain removal network is composed of dilated convolution and channel attention. This combination can explore the correlation between features from the dimensions of spatial and channel. Meanwhile, for the lower detail restore network, we construct a simple yet effective symmetrical encoder-decoder structure to prevent the loss of global structures information and encourage the details back. Furthermore, our network is end-to-end trainable, easy to implement and without giant parameter quantity. Extensive experiments on synthetic and real-world datasets have shown that our DRAEN achieves better accuracy and visual improvements against recent state-of-the-art methods.
Keywords: Single image deraining, encoder-decoder network, image processing, feature fusion
