卷积神经网络用于改进基于事件的Shack-Hartmann波浪前沿重建
Applied optics
|June 10, 2024
概括
本研究介绍了一个基于事件的波浪前线网络 (EBWFNet),用于更快,更准确的Shack-Hartmann波浪前线传感 (SHWFS). 在现实世界中,新型CNN在现实条件下实现了亚像素精度,超过了现有的基于事件的方法.
科学领域:
- 光学和光子学 在光学和光子学.
- 机器学习 机器学习
- 适应光学适应光学
背景情况:
- 沙克-哈特曼波面传感 (SHWFS) 传统上使用基于的摄像头进行偏差测量.
- 传统方法受到固定采样率和低效的像素使用限制.
- 基于事件的摄像头为SHWFS提供异步,高速的数据采集.
研究的目的:
- 开发一种新的卷积神经网络 (CNN),用于基于事件的SHWFS的实时,准确的点心部估计.
- 在现实场景中评估拟议的EBWFNet的性能.
- 将EBWFNet与基于事件的SHWFS技术进行比较.
主要方法:
- 开发一个定制的SHWFS硬件与同步和基于事件的摄像头.
- 使用CNN架构实现基于事件的波浪网络 (EBWFNet).
- 使用基于的摄像头数据进行EBWFNet的无监督培训和测试.
- 实地测试和废弃性研究,以评估性能和组件影响.
主要成果:
- 在现实世界条件下,EBWFNet实现了高度准确的,分像素点心脏估计.
- 与现有的基于事件的先进SHWFS方法相比,已显著改进.
- 一个未经优化的MATLAB实现在单个GPU上实现了超过800Hz的速度.
结论:
- 拟议的EBWFNet显著提高了基于事件的SHWFS的准确性和速度.
- 基于事件的摄像头与CNN相结合,代表了适应光学的一个有希望的进步.
- 该EBWFNet提供了一个强大的和高效的解决方案,用于实时波浪偏差测量.
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