HNDiff: Haze-Noise Diffusion for Image Dehazing

Existing diffusion-based methods have recently made signif-icant progress in image dehazing. However, they typically neglect thephysics of haze formation and reconstruct clean images from pure Gaus-sian noise, thereby limiting their restoration potential. To address thisissue, we propose Haze-Noise Diffusion (HNDiff), a novel diffusion frame-work that embeds the atmospheric scattering model as an inductive bias.By grounding diffusion in physical principles, HNDiff ensures that therestoration aligns more closely with underlying mechanisms of haze for-mation. In its forward process, we introduce joint haze-noise diffusionwith a haze-aware noise scheduler, which progressively adds both hazeand noise to an image. Essentially, the scheduler adapts noise levels ac-cording to haze density, meaning that regions with heavier haze receivestronger noise injection to encourage content generation, while clearer re-gions receive lighter noise to better preserve details, which directly linksthe forward degradation process with the physics of haze. In the reverseprocess, we then derive a physically consistent dehazing-denoising pro-cess that simultaneously removes haze and noise to restore a clean imagein a manner aligned with the forward degradation process. To furtherenhance practicality, we propose Latent HNDiff, which compiles cleanlatent priors that can be seamlessly integrated into existing dehazing net-works to boost performance. Extensive experiments show that our worksignificantly improves leading dehazing backbones and achieves state-of-the-art results on benchmark datasets. The project page is available athttps://jin-ting-he.github.io/HNDiff/.