从使用斜边空间频率响应作为度度指标的交通标志进行定量内核估计
Amit Pandey1, Mohd Zubair Akhtar2, Nandana Kappuva Veettil2
1University of Applied Sciences, Institute of Innovative Mobility (IIMo), Research group Sensor Technology and Data Fusion for Environmental Perception, Esplanade 10, Ingolstadt, 85049, Germany. amit.pandey@thi.de.
这项研究引入了一种新方法,用主要组件分析 (PCA) 和差异演变优化来估计汽车摄像头模糊内核. 这种技术可以有效地监测相机度下降的状态.
科学领域:
- 光学工程的光学工程.
- 图像处理 图像处理
- 汽车技术 汽车技术
背景情况:
- 摄像头的清晰度对于汽车应用至关重要,通过空间频率响应 (SFR) 在线末端 (EOL) 测试中进行评估.
- 估计模糊内核是实现汽车摄像头实时状态监控的关键一步.
研究的目的:
- 开发和验证一种用于估计汽车摄像机模糊内核的方法.
- 为了能够在现场监控相机度下降.
主要方法:
- 在Zemax生成的合成内核上利用主要组件分析 (PCA),构建了一个具有1300个空间变异点扩散函数 (PSF) 的模型.
- 开发了一种使用合成图像 (带有交通标志的卷曲内核) 进行训练和真实数据进行验证的算法.
- 采用差异演变优化来最大限度地减少模糊的参考ROI和内核之间的SFR差异,确定最匹配的内核.
主要成果:
- 在内核估计中实现了高准确度,真实和估计内核之间的结构相似性指数测量 (SSIM) 从0.92到0.98.
- 在真实世界汽车摄像头图像上的验证显示,SSIM>0.82用于估计与模糊ROI.
- 以皮尔森相关系数 (0.84-0.99) 和小数相似性 (0.86-0.99) 证明了有前途的性能.
结论:
- 拟议的方法准确地估计了汽车摄像机模糊内核.
- 这种内核估计技术是对汽车摄像头现场状态监控的可行第一步.
- 该方法显示了随着时间的推移跟踪度降解的潜力.
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