Physics Meets Perception: A Reinforcement Learning Framework for Unpaired Real-World Image Dehazing

Real-world image dehazing remains a challenging problem due to the significant domain gap between synthetic training data and natural haze, often leading to poor generalization. While unsupervised Generative Adversarial Networks (GANs) attempt to bridge this gap, they frequently suffer from training instability and hallucinatory artifacts. Recently, Reinforcement Learning (RL) has emerged as a promising alternative; however, current RL-based restoration paradigms predominantly rely on diffusion models, leading to prohibitive computational costs and ineffective exploration. To address these bottlenecks, we propose Dehaze-RL, an efficient framework tailored for unpaired real-world dehazing. Bypassing the expensive iterative sampling of diffusion models, we design an efficient policy network to predict hybrid actions in a Physics Hybrid Action Space. By explicitly estimating physical parameters, enhancement factors, and a gating policy map, we anchor the agent’s exploration in reliable physical priors, effectively resolving the exploration dilemma. These components are dynamically integrated via a gating fusion mechanism. Furthermore, we introduce a Multi-Granularity Reward Mechanism to provide comprehensive feedback, seamlessly aligning the outputs with human perception without structural distortions. Extensive experiments demonstrate that Dehaze-RL outperforms stateof-the-art methods in both visual fidelity and quantitative metrics.