Towards Multiple-in-One Image Deraining via Scale-Aware Trident Transformer Network

Despite significant progress has been made in image deraining, most methods only independently handle a single type of rain degradation (e.g., rain streak, raindrop or nighttime rain), which limits the model's capacity to adapt to real-world dynamic degradations, especially on all-time autonomous driving. To advance this field, we introduce a new task: multiple-in-one image deraining, which aims to simultaneously address multiple types of rain degradation using a universal mix-trained model. To benchmark this task, we first construct a high-quality dataset based on driving scenarios, called MIO-Rain, which contains four patterns: daytime rain streak, daytime raindrop, nighttime rain streak and nighttime raindrop. Furthermore, we develop an effective scale-aware trident Transformer network (STTformer) for multiple-in-one image deraining. The proposed framework integrates coarse-to-fine and multi-patch parallel multi-branch architectures, with each branch employing multi-scale learning for its robustness to diverse rain appearances. Extensive experiments demonstrate the effectiveness of our model, and show that it achieves favorable performance against state-of-the-art ones.