An Unsupervised Image Dehazing With Scene Geometry Prior for Road Traffic Scenarios

Despite advances in single image dehazing, robust dehazing for real-world road traffic scenes remains challenging due to scarce paired data, traffic-specific geometry, and real-time constraints. To address this issue, we propose a novel image prior for road traffic scenes, termed scene geometry prior (SGP), which leverages depth cues derived from vanishing point (VP) to provide geometry-aware guidance and reduce reliance on paired training data. Our SGP comprises two components: a global SGP (G-SGP) that captures the global geometric distribution and a non-local SGP (NL-SGP) that corrects the errors, among obstructions belonging to the same category, in captured global distribution. Building on the proposed prior, we develop a lightweight and unsupervised road traffic image dehazing network (RTDnet). It consists of a main sub-network guided by the G-SGP to reconstruct the haze-free image, alongside two auxiliary sub-networks that leverage the NL-SGP and VP information to respectively estimate transmission map, and atmospheric light. During training, we introduce an atmospheric scattering model (ASM)-driven mutual-boost learning mechanism (ASM-ML), which is rooted in Bayesian theory and effectively integrates the strengths of different priors without mutual interference while distilling ASM-based physical knowledge into each sub-network. By coupling SGP with ASM-ML, RTDnet can be trained without paired traffic data by exploiting traffic-specific geometry, whose accurate guidance reduces the reliance on large model capacity and enables lightweight real-time deployment. Experiments demonstrate that our RTDnet surpasses state-of-the-art competitors in terms of restoration quality, efficiency, and model size. Moreover, its robust dehazing performance benefits downstream tasks operating in hazy conditions.