论文标题
深度神经网络，用于在波长转移干涉仪中有效相位的有效相位解调
Deep neural networks for efficient phase demodulation in wavelength shifting interferometry
论文作者
论文摘要
光学干涉法中的分析相解调算法通常无法达到CRAMér-RAO结合（CRB）设置的理论灵敏度极限。我们表明，深度神经网络（DNNS）可以通过在不受常规算法（例如噪声统计量和参数约束）培训的新信息培训时，通过大量边缘实现或超过CRB来执行有效的相位解调。例如，我们开发并应用了DNNS到波长转移干涉仪上。当接受噪声统计训练时，DNNS在相位灵敏度方面优于常规算法，并实现传统的三个参数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。对于牢固的参数，DNN的相位灵敏度甚至可以接近我们称为单个参数CRB的基本限制。这种敏感性的提高可以转化为无需修改的单噪声比率的显着增加，或者用于放松硬件要求。
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.
