Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration

The recent study on nighttime image restoration highlights the challenges posed by multiple adverse weather conditions, which often coexist with varying lighting effects. The ClearNight framework emerges as a solution, utilizing Retinex-based dual priors to effectively remove complex degradations in nighttime images. This framework not only enhances restoration effectiveness but also achieves state-of-the-art performance on both synthetic and real-world images. The introduction of the AllWeatherNight dataset, featuring high-quality nighttime images with diverse degradations, supports this research, providing a valuable resource for further advancements in the field. By addressing the practical yet under-explored problem of nighttime image restoration, this work opens new avenues for applications in areas such as surveillance, navigation, and environmental monitoring, where clear imagery is essential.