Physics-Guided Posterior Sampling for Diffusion-Based Real-World Dehazing and Image Enhancement

Real-world image dehazing is a challenging task due to the collection of aligned hazy/clear image pairs under unpredictable and complex environments. To address this limitation, we propose a Physical-Guided Posterior Sampling (PGPS) method that designs a dehazing reconstruction posterior to sample an RGB and depth from pre-trained unconditional diffusion generation process. First, we introduce a Hybrid Degradation Atmospheric Scattering Model (HD-ASM) to adapt the diffusion model, enabling the generation of high-fidelity dehazed images from posterior samples without relying on the aligned hazy/clear image pairs. Second, we propose a two-stage sampling strategy with piecewise loss to improve sampling quality and stability, along with a post-processing technique to remove JPEG compression artifacts amplified by dehazing. Extensive experiments show that our method outperforms state-of-the-art techniques in image dehazing, and in the RTTS dataset’s complex human-vehicle environment. Additionally, our approach also surpasses other benchmarks in object detection, exhibiting superior generalization performance.