Image Dehazing in the Era of Generative AI: A Survey

Abstract

The optimization objective in image dehazing is fundamentally shifting from deterministic pixel mapping to high-dimensional probability distribution modeling. This survey organizes the field according to three optimization paradigms: physical prior-guided deterministic mapping, end-to-end feature reconstruction, and generative distribution alignment. Our synthesis yields three main findings. First, single-pass CNN, Transformer, and state-space models remain attractive for latency-sensitive applications, although pixel-wise objectives can suppress high-frequency and perceptually plausible details. Second, GAN- and diffusion-based methods often report improved perceptual or distributional quality when evaluated using LPIPS or FID, but iterative sampling and weak physical anchoring increase computational cost and the risk of structurally inconsistent details under dense haze. Third, heterogeneous datasets, image resolutions, evaluation protocols, and incomplete perceptual reporting do not currently support a controlled quantitative comparison of hallucination rates across architectures. We therefore analyze the role of atmospheric scattering constraints in anchoring generative trajectories and advocate physics-consistent, task-driven evaluation. Future work should develop unified hallucination benchmarks and efficient, physically constrained generative models for edge and safety-critical deployment.