HD-Net: Hierarchical Distillation Network for High-Efficiency Single Image Deraining

Rain streaks usually result in severe image visual degradation and foreground occlusion, affecting the quality of computer tasks in outdoor scenes. Currently, the mainstream methods in single-image deraining are based on data-driven. However, the deep learning network could be imperfect, with limited power for learning the global information from rain streaks all over the map. In order to solve this problem, we proposed a novel Hierarchical Distillation Network (HDNet). In this network, Hierarchical Feature Extraction Block (HFEB) can fully utilize the Transformer's learning ability in high-level features, integrate local detail extraction and global structure representation, and compensate for the weakness of the Convolutional Neural Network (CNN), which is overattentive to the underlying image features. Furthermore, the Distillation-Calibration Block (DCB) are adopted to avoid feature redundancy during model training and calibrate the channel and spatial information through the feature transmission, which could significantly improve the learning efficiency. Finally, the experiment results show that our model performs better than traditional CNN models and state-of-the-art methods. © 2022 IEEE.