Enhancing Image Deraining Through VLM-Based Data Refinement and Classification

Image deraining has gained significant attention in recent years due to its essential role in applications like autonomous driving and surveillance systems. Despite their good performance in rain removal, most image-deraining models are trained on synthetic datasets, leading to a performance gap when applied to real-world scenarios due to differences between synthetic and actual rain patterns. Although some real-world datasets exist, they may contain unsuitable images for training image-deraining models, such as those without clear rain or containing haze, which could lead to suboptimal model performance. Therefore, we propose Automated Understanding and Refinement Agents (AURA) to refine deraining datasets by leveraging large vision-language models to filter out inappropriate training data and categorize images based on the rain density (e.g., light, moderate, and heavy rain). This automated refinement and annotation process enhances dataset quality, resulting in improved deraining model performance in real-world scenarios. Experimental results demonstrate that training deraining models on datasets refined and annotated by AURA can significantly enhance their deraining performance.