Towards Universal Rainy Image Restoration: Benchmark and Baseline

Abstract

Despite significant progress has been made in image deraining, most existing methods are limited to handling only a single type of rain degradation or a specific pattern of rain. However, real-world rain scenarios tend to contain diverse rainy patterns due to variations in the rainfall process and lighting conditions. To address this dilemma and advance this field, we introduce a new task: Universal Rainy Image Restoration (URIR), which aims to handle multiple types of rain degradation on a single model. To benchmark this task, we construct a high-quality dataset called URIR-8K, which contains four patterns: rain streak, raindrop, rain accumulation and nighttime rain. Building upon this dataset, we present a comprehensive study on existing approaches by evaluating their universal deraining capabilities and their effect on downstream object detection task. In addition, we design a multi-scale vision Mamba as a baseline model, leveraging the benefits of multi-scale learning for its robustness to diverse rain appearances. Unlike existing methods that use fixed-scale scanning for feature extraction, we employ a multi-scale 2D scanning technique to better help image restoration in the richer scale space. Extensive experimental analysis shows the potential of our proposed task and the effectiveness of our model.