Fast Model-Free Image Dehazing via Haze-Density-Driven Fusion
Jeonghyeon Son, Dat Ngo, Suhun Ahn, Bongsoon Kang · Mathematics · 2026
This paper presents a fast and model-free image dehazing algorithm based on haze-density-driven image fusion. Instead of relying on explicit physical haze models, the proposed approach restores visibility by fusing the input image with its dehazed estimate using spatially adaptive weights derived from a haze-density map. The dehazed estimate is produced by blending multiple synthetically under-exposed versions of the input, where local fusion weights promote stronger enhancement in dense-haze regions while preserving appearance in mild-haze areas. This model-free formulation avoids the limitations inherent to traditional scattering-based models and ensures robust performance under spatially nonuniform haze conditions. The overall framework is lightweight and suitable for embedded, real-time imaging systems due to its reliance on simple local operations. Experimental evaluations demonstrate that the proposed method achieves competitive results compared to state-of-the-art dehazing algorithms in both visual quality and quantitative metrics. A hardware prototype further shows that the method can process high-resolution imagery at real-time rates, achieving 271.74 megapixels per second, or 30.69 frames per second at DCI 4K (4096×2160) resolution. These results establish haze-density-driven fusion as an effective and efficient model-free solution for real-time image dehazing.