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    科学领域:

    • 光学工程的光学工程.
    • 机器学习 机器学习
    • 图像处理 图像处理

    背景情况:

    • 多镜头成像系统的精确对齐对于性能至关重要.
    • 传统的对齐方法耗时,需要专门的设备.
    • 需要自动化和可扩展的解决方案来诊断错位.

    研究的目的:

    • 介绍两种互补的基于深度学习的反向设计方法,用于诊断多元镜头系统中的错位.
    • 为了使错位诊断仅使用光学测量.
    • 改进制造和质量控制在精密成像.

    主要方法:

    • 使用射线跟踪点图来预测6镜头摄影中五度自由度 (5-DOF) 的错误.
    • 开发基于物理的模拟管道,使用灰度合成摄像头图像.
    • 采用深度学习模型来估计两个和六个镜头系统中的下降和倾斜误差 (4-DOF).

    主要成果:

    • 通过使用点图,实现了0.031mm的横向转换和0.011°倾斜的平均绝对误差,用于使用6镜头系统的6镜头系统.
    • 通过使用合成摄像头图像,成功估计了双镜头和六镜头系统中的4-DOF,衰减和倾斜误差.
    • 证明了深度学习对自动化错位诊断的有效性.

    结论:

    • 基于深度学习的反向设计为诊断镜头系统错位提供了一个有希望的自动化解决方案.
    • 这些方法可以显著提高光学制造和质量控制的精度和效率.
    • 提出的技术有可能重塑精确成像领域.