GenHaze: Pioneering Controllable One-Step Realistic Haze Generation for Real-World Dehazing

Sixiang Chen, Tian Ye, Yunlong Lin, Yeying Jin, Yijun Yang, Haoyu Chen, Jianyu Lai, Song Fei, Zhaohu Xing, Fugee Tsung, Lei Zhu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 9194-9205

Real-world image dehazing is crucial for enhancing visual quality in computer vision applications. However, existing physics-based haze generation paradigms struggle to model the complexities of real-world haze and lack controllability, limiting the performance of existing baselines on real-world images. In this paper, we introduce GenHaze, a pioneering haze generation framework that enables the one-step generation of high-quality, reference-controllable hazy images. GenHaze leverages the pre-trained latent diffusion model (LDM) with a carefully designed clean-to-haze generation protocol to produce realistic hazy images. Additionally, by leveraging its fast, controllable generation of paired high-quality hazy images, we illustrate that existing dehazing baselines can be unleashed in a simple and efficient manner. Extensive experiments indicate that GenHaze achieves visually convincing and quantitatively superior hazy images. It also significantly improves multiple existing dehazing models across 7 non-reference metrics with minimal fine-tuning epochs. Our work demonstrates that LDM possesses the potential to generate realistic degradations, providing an effective alternative to prior generation pipelines.