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哈兹迪夫 (Hazediff):一种无训练的基于扩散的图像脱雾方法,具有像素级特征注入

Xiaoxia Lin¹, Zhengao Li¹, Dawei Huang¹

¹College of Intelligent Equipment, Shandong University of Science and Technology, Taian, China.

|October 28, 2025

概括

HazeDiff是一种无需培训的图像清除方法,通过消除对数据集的需求,克服了当前方法的局限性. 这种基于扩散模型的技术提高了图像质量和概括性,以实现更清晰的视觉任务.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
人工智能的人工智能

背景情况:

经常出现的雾现象会降低图像质量,妨碍中高层次的视觉任务.
当前的图像处理方法在数据采集和概括方面面临着挑战.

研究的目的:

提出HazeDiff,一种基于扩散模型的无培训的图像脱雾方法.
克服现有的数据依赖的除尘技术的局限性.

主要方法:

哈兹迪夫使用了扩散模型,消除了对配对训练数据的需求.
像素级特征注入 (PFI) 将参考图像特征集成到扩散过程中.
结构保留模型 (SRM) 增强特征并保持结构完整性.

主要成果:

在真实世界和合成数据集上,HazeDiff超越了最先进的方法.
在没有参考 (NIQE) 和完全参考 (PSNR) 指标上获得更高的分数.
在恢复高质量的图像方面表现出卓越的概括能力和实用性.

结论:

哈泽迪夫提供了一个可靠和有效的解决方案,用于图像 dehazing.
无培训方法降低了计算成本,并提高了模型稳定性.
恢复后的图像呈现出自然的视觉特征和清晰的结构内容.

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