深度神经网络用于波长偏移干涉测量中的高效相位解调
Deep neural networks for efficient phase demodulation in wavelength shifting interferometry
摘要：
光学干涉测量中的解析相位解调算法通常无法达到由Cramér-Rao界（CRB）设定的理论灵敏度极限。我们表明，深度神经网络（DNN）可以通过在使用传统算法未使用的新信息（如噪声统计和参数约束）进行训练时达到或超过CRB来执行有效的相位解调。作为一个例子，我们开发和应用DNN波长漂移干涉。当使用噪声统计进行训练时，DNN在相位灵敏度方面优于传统算法，并实现了传统的三参数CRB。此外，通过将参数约束纳入训练集，它们可以超过传统的CRB。对于约束良好的参数，DNN的相位灵敏度甚至可以接近我们称为单参数CRB的基本极限。这样的灵敏度改进可以转化为信噪比的显著增加而无需硬件修改，或者用于放宽硬件要求。
英文摘要：
Analytical phase demodulation algorithms in optical interferometry typically fail to reach the theoretical sensitivity limit set by the Cramér-Rao bound (CRB). We show that deep neural networks (DNNs) can perform efficient phase demodulation by achieving or exceeding the CRB by significant margins when trained with new information that is not utilized by conventional algorithms, such as noise statistics and parameter constraints. As an example, we developed and applied DNNs to wavelength shifting interferometry. When trained with noise statistics, the DNNs outperform the conventional algorithm in terms of phase sensitivity and achieve the traditional three parameter CRB. Further, by incorporating parameter constraints into the training sets, they can exceed the traditional CRB. For well confined parameters, the phase sensitivity of the DNNs can even approach a fundamental limit we refer to as the single parameter CRB. Such sensitivity improvement can translate into significant increase in single-to-noise ratio without hardware modification, or be used to relax hardware requirements.
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