Using Mask-Based Enhancement and Feature Aggregation for Single Image Deraining

Image rain removal is an essential and challenging low-level task in computer vision. In recent years, although great progress has been achieved in the field of image and video deraining, the restored details may be incomplete in many cases, making it difficult to recover the clear images from diverse rain forms. Moreover, the features of layers may not be fully exploited, which may reduce the rain removal performance to some degree. To address the above issues, we propose a novel Mask-based Enhancement and Feature Aggregation Network (MEFA-Net). First, we develop a novel structure with enhancement branch, which can greatly improve the feature representation capability. The enhancement branch is implemented based on the random masking technique, which can generate complementary features and improve the robustness. Specially, we design a novel adaptive weighting block in the MEFA-Net, which can adaptively assign weights to the enhanced features for each specific image, effectively improving the generalization ability of MEFA-Net. Second, we propose a feature aggregation sub-module (FAS) for comprehensive representation by integrating the features from different layers. Experimental results demonstrate that the proposed MEFA-Net can achieve better performance than most state-of-the-art image deraining methods on four synthetic and one real-world datasets.