Comparison of Dehazing Algorithms Using Real-World Hazy Images
Chaobing Zheng, Xianfeng David Gu, Qingping Hu, Wenjian Ying, Weihe Li, Shiqian Wu · 2025
Single image dehazing has been widely studied by using physics-driven, data-driven, and neural augmentation methods. In this paper, their typical candidates are compared by using real-world hazy images because most data-driven and neural augmentation methods are trained by using synthetic hazy images. Experimental results on real-world hazy images indicated that that physics-driven single image dehazing algorithms lack robustness, while data-driven ones can handle thin hazy images well but perform poorly in dense hazy conditions. Neuralaugmentation algorithms effectively combine the strengths of both approaches, providing a better choice.