Unlocking Joint Image Deraining and Low-Light Enhancement: Benchmark and Baseline

摘要

Rainy weather typically leads to significantly reduced ambient illumination due to overcast skies.However, most existing image deraining datasets overlook this critical physical condition.They are usually constructed by linearly superimposing rain layers onto clean background images, without accounting for illumination degradation.This simplification introduces a clear domain gap between synthetic and real-world rainy images, thus limiting the generalization capability of current deraining algorithms.Moreover, existing methods predominantly focus on removing rain streaks while ignoring the simultaneous degradation caused by low-light conditions.To address these limitations, we introduce a new joint task: image deraining and low-light enhancement.Specifically, we construct a physically plausible dataset that simulates rainy scenes under low-light conditions, incorporating both rain streaks and raindrops with illumination-aware degradation modeling.In addition, we propose a baseline deraining network based on a multi-scale Mamba architecture, which jointly restores rain-free and well-lit images by effectively modeling both global illumination and local rain interference.Extensive experiments demonstrate that our method outperforms existing deraining approaches.The proposed dataset is released at https://drive.google.com/file/d/1QXxHqpYL7Q1TR5tdvAHm8tV2BdZgpGOc/view?usp=sharing.