Real-World Nighttime Image Dehazing via Bayesian-Based Fractional-Order Variational Model

Images captured under real-world nighttime haze conditions often suffer from severe degradations, including low visibility, color distortion, and reduced contrast, which not only impair visual perception but also degrade the performance of vision-based tasks. However, existing dehazing methods are mainly designed for daytime scenarios and struggle to cope with the complex illumination and scattering characteristics of nighttime hazy images. In this paper, we propose a novel Bayesian-based variational framework with fractional-order constraints for real-world nighttime image dehazing. First, a simplified physical model is constructed to characterize nighttime hazy images, accounting for haze, low-light conditions, Poisson noise, and glow degradations. An anisotropic pre-processing strategy is iteratively applied in the Lab color space to remove glow effects. Subsequently, illumination and reflectance estimation within our constructed physical model is formulated as a maximum a-posteriori (MAP) problem, which is then approximated as a unified variational optimization function. To impose prior constraints, two fractional-order terms are introduced as priors to regulate the illumination and reflectance, promoting piecewise smoothness in illumination and preserving sharp edges and fine textures in reflectance. The resulting variational model is efficiently solved using the alternating direction minimization method. Finally, the estimated illumination and reflectance are enhanced via spatial-domain gamma correction for brightness adjustment and frequency-domain processing for texture detail enhancement. Extensive experiments on real-world datasets demonstrate that the proposed framework outperforms state-of-the-art dehazing methods in both qualitative and quantitative evaluations. Besides, our algorithm generalizes effectively to both other degraded scenes and high-level vision tasks.