Single Image Deraining Using Residual Channel Attention Networks

Image deraining is a highly ill-posed problem. Although significant progress has been made due to the use of deep convolutional neural networks, this problem still remains challenging, especially for the details restoration and generalization to real rain images. In this paper, we propose a deep residual channel attention network (DeRCAN) for deraining. The channel attention mechanism is able to capture the inherent properties of the feature space and thus facilitates more accurate estimations of structures and details for image deraining. In addition, we further propose an unsupervised learning approach to better solve real rain images based on the proposed network. Extensive qualitative and quantitative evaluation results on both synthetic and real-world images demonstrate that the proposed DeRCAN performs favorably against state-of-the-art methods.