单眼复杂振幅成像通过一个极化多重复合液晶透镜信息里埃神经网络
Liu Li1,2, Minghao Liao3,4, Yixin Zhang1
1State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
National science review
|January 29, 2026
概括
这项研究引入了一种用于复杂振幅成像的新型摄像系统,消除了对标记数据的需求. 该系统使用液晶透镜信息的里埃神经网络,用于高保真性,单次重建光场信息.
科学领域:
- 计算成像技术的成像
- 光学是什么?光学是什么?光学是什么?
- 机器学习 机器学习
背景情况:
- 用于成像的深度学习需要大型标记数据集.
- 基于物理学的神经网络可以实现无数据训练,但面临复杂任务的硬件限制.
- 同时获取复杂的振幅光场信息是具有挑战性的,因为硬件限制限制了分辨率.
研究的目的:
- 开发一个无数据,高分辨率的复杂振幅成像系统.
- 克服传统成像硬件对于光场信息采集的局限性.
- 为先进的成像应用提出一个新的硬件算法框架.
主要方法:
- 一个单眼相机整合了一个液晶 (LC) 镜头信息里埃神经网络.
- 使用一个极化多重复的双焦LC镜头和一个极化图像传感器.
- 该摄像头作为一个偏振相位移辐射剪切干扰仪,用于无数据重建.
主要成果:
- 不同场景的高保真,单拍复杂幅度重建,没有标记数据.
- 用 λ/35 阶段精度进行波面偏差重建 (136 个泽尼克模式) 的实验演示.
- 成功检索静态全息图和动态监控空气流和火焰场.
结论:
- 拟议的硬件算法框架可以实现紧,多功能和高性能复杂的振幅成像.
- 这种方法为适应光学,全息图重建和材料诊断中的应用提供了有前途的途径.
- 该系统在复杂的成像任务中实现了高时空分辨率和精度.
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