解决合成到真实图像去雾的域差异问题，提升实际场景效果。
A Synthetic-to-Real Dehazing Method based on Domain Unification
- 通过统一真实与合成域的物理关系，缓解分布偏移。
- 在真实图像上性能显著优于现有方法。
- 适合需要高精度去雾的自动驾驶与遥感应用。
由于分布偏移，基于深度学习的图像去雾方法在处理真实世界雾霾图像时性能下降。本文发现，这种真实与合成域之间的偏差源于干净图像采集不完善：场景复杂性和深度效应导致采集的干净图像无法严格满足理想条件，使真实域与合成域的大气物理模型不一致。为此，提出一种基于域统一的合成到真实去雾方法，旨在统一两个域的关系，使去雾模型更贴近实际。大量定性与定量实验表明，该方法在真实图像上的表现显著优于当前最优方法。
原文摘要 · Abstract (English)
Due to distribution shift, the performance of deep learning-based method for image dehazing is adversely affected when applied to real-world hazy images. In this paper, we find that such deviation in dehazing task between real and synthetic domains may come from the imperfect collection of clean data. Owing to the complexity of the scene and the effect of depth, the collected clean data cannot strictly meet the ideal conditions, which makes the atmospheric physics model in the real domain inconsistent with that in the synthetic domain. For this reason, we come up with a synthetic-to-real dehazing method based on domain unification, which attempts to unify the relationship between the real and synthetic domain, thus to let the dehazing model more in line with the actual situation. Extensive experiments qualitatively and quantitatively demonstrate that the proposed dehazing method significantly outperforms state-of-the-art methods on real-world images.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。