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通过染器进行夜间图像消光

Zheyan Jin¹, Huajun Feng¹, Zhihai Xu¹

¹State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou 310027, China.

Journal of imaging

|August 25, 2023

概括

由于复杂的光干扰,夜间图像脱光具有挑战性. 这项研究引入了一种新的数据合成和网络改进,以实现更准确,更有效的夜间雾清除.

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:

由于人工灯的雾不均,导致大气光,发光和直接光线干扰,夜间图像难以消除雾.
准确区分和去除复杂的散射雾是具有挑战性的.
获取高清夜间雾移除数据集是有问题的.

研究的目的:

开发一个有效的方法,用于夜间的图像 dehazing.
为应对夜间场景中复杂的雾干扰和数据稀缺的挑战.
为了提高夜间雾清除算法的准确性和效率.

主要方法:

引入了一个雾散射公式来模拟3D雾分布.
提出了一种新的数据合成方法,使用CG纹理和光源照明与光线跟踪.
将3D雾散射转换为用于神经网络学习的2D图像数据集.
改进了一个神经网络,并建立了一个基于光学PSF的夜间雾强度评估标签.

主要成果:

在夜间除实验中获得了卓越的视觉效果和客观指标.
与现有方法相比,证明了计算速度的提高.
成功地将3D雾散射关系映射到2D图像数据集中,以增强学习.

结论:

拟议的数据构建和网络改进有效地解决了夜间图像破坏挑战.
这种新的方法导致视觉质量和处理效率的显著改善.
这项工作提供了一个强大的解决方案,用于提高夜间图像的可见性.

关键词:

数据生成数据的数据生成.图像被破坏了图像被破坏了图像恢复图像恢复图像恢复

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