  To advance research in learning-based defogging algorithms, various synthetic
fog datasets have been developed. However, existing datasets created using the
Atmospheric Scattering Model (ASM) or real-time rendering engines often
struggle to produce photo-realistic foggy images that accurately mimic the
actual imaging process. This limitation hinders the effective generalization of
models from synthetic to real data. In this paper, we introduce an end-to-end
simulation pipeline designed to generate photo-realistic foggy images. This
pipeline comprehensively considers the entire physically-based foggy scene
imaging process, closely aligning with real-world image capture methods. Based
on this pipeline, we present a new synthetic fog dataset named SynFog, which
features both sky light and active lighting conditions, as well as three levels
of fog density. Experimental results demonstrate that models trained on SynFog
exhibit superior performance in visual perception and detection accuracy
compared to others when applied to real-world foggy images.
