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    科学领域:

    • 光学和光子学 在光学和光子学.
    • 人工智能的人工智能
    • 信号处理 信号处理

    背景情况:

    • 垂直扫描白光干扰仪 (SWLI) 是高分辨率表面造型的关键技术.
    • 传统的SWLI需要高的抽样率,导致大量的数据和缓慢的获取.
    • 亚尼奎斯特采样为更快的测量提供了途径,但往往会损害准确性.

    研究的目的:

    • 开发一种基于深度学习的方法,用于SWLI中的亚尼奎斯特采样.
    • 从低采样的干涉测量信号中提取准确的表面形状.
    • 提高SWLI测量的效率,准确性和稳定性.

    主要方法:

    • 引入带有通道智能值的封面深剩余收缩网络 (E-DRSN-cw).
    • 使用深度学习模型从低样本信号中提取过量采样信封.
    • 实施软门,零填充,层规范化和回归信封预测,以加强培训.

    主要成果:

    • 与经典方法相比,E-DRSN-cw模型在模拟和实验中表现出优越的性能.
    • 在SWLI测量的准确性和稳定性方面取得了显著的改进.
    • 验证了深度学习方法在处理低样本干涉度数据方面的有效性.

    结论:

    • E-DRSN-cw方法为SWLI提供了快速而准确的测量解决方案.
    • 这种深度学习方法消除了对信号或系统的预先知识的需求.
    • 允许高效的高分辨率表面造型,降低数据采集要求.