Hyperspectral compressive wavefront sensing
Hyperspectral compressive wavefront sensing
复制标题
高光谱压缩波前传感
DOI：
10.1017/hpl.2022.35
复制
发表时间：
2023-03-21
影响因子：
4.8
通讯作者：
Doepp, Andreas
中科院分区：
文献类型：
--
作者：
Howard, Sunny;Esslinger, Jannik;Doepp, Andreas
提出的是将快照压缩成像和侧面剪切干涉仪结合起来的一种新颖方法，以便在单个镜头中捕获超短激光脉冲的空间谱相。由于其参数效率和相对于其他方法的较高速度，因此将深层展开算法用于快照压缩成像重建，可能允许在线重建。该算法的正则化项使用具有3D卷积层的神经网络表示，以利用激光波前存在的时空光谱相关性。压缩传感通常不适用于调制信号，但我们在此证明了它的成功。此外，我们训练一个神经网络，从Zernike多项式方面预测侧面剪切干涉图的波前，这再次增加了我们技术的速度而不牺牲忠诚度。基于仿真的结果支持此方法。虽然应用于横向剪切干涉法的示例，但此处介绍的方法通常适用于广泛的信号，包括Shack-Hartmann型传感器。结果可能超出激光波前表征的背景，包括定量相成像。
Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm's regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts. Compressed sensing is not typically applied to modulated signals, but we demonstrate its success here. Furthermore, we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials, which again increases the speed of our technique without sacrificing fidelity. This method is supported with simulation-based results. While applied to the example of lateral shearing interferometry, the methods presented here are generally applicable to a wide range of signals, including Shack-Hartmann-type sensors. The results may be of interest beyond the context of laser wavefront characterization, including within quantitative phase imaging.