Abstract: Scene recovery, the restoration of images degraded by adverse weather conditions, presents significant challenges for existing methods. Physical models, constrained by their inherent assumptions, often fail when these assumptions are not met; Deep learning models are powerful, they are limited by the diversity of their training datasets, leading to poor generalization and high computational demands. To address these limitations, we propose the Luminous Flux Prior (LFP), to recover degraded images under diverse adverse weather without learning. Luminous flux, a physical measure that reflects image brightness, has a rate of change that demonstrates a significant correlation with transmission. Consequently, we leverage this rate of change in luminous flux as prior knowledge to estimate transmission, which in turn assists in image recovery. This approach reduces dependency on physical parameters and enhances adaptability to various weather. Experimental validation under diverse conditions, such as sandstorms, underwater environments, and haze, attests to the robustness of LFP in restoring clear images. With a time complexity of $\mathcal{O}(N\log N)$, LFP enables real-time recovery, making it a suitable for devices with limited computational resources.