一种基于神经网络的宽带光源相位重建方法

A Phase Reconstruction Method of Broadband Light Source Based on Neural Network

相位定量检测是检测入射光场波前的重要指标,Shack-Hartmann波前传感器能够同时测量波前的振幅和相位分布,但其分辨率较低。针对传统的非干涉相位检测法需要两台放置在不同位置的相机,使用两幅图像之间的相位差来估算波前的相位信息,造成了相位成像系统的复杂性和不确定性的问题,提出了将宽带光源作为照明光源,只需单次拍摄彩色图像输入神经网络并使用梯度下降算法进行优化的相位重建方法。仿真和试验结果表明,这项技术将物理模型和神经网络相结合,能够以高分辨、高速度、低成本实现待测物体的相位重建工作。其中作为评价重建图像质量的均方误差函数(MSE)最小可达到0.089 rad,对于透明细胞的相位重建质量为0.133 rad,要优于GS法(0.320 rad)和TIE法(0.378 rad)。因此,提出的基于神经网络的宽带光源相位重建方法可以运用于自适应光学、活细胞生物实时成像等领域。

Quantitative phase detection is an important indicator for detecting the wavefront of incident light field.Shack-Hartmann wavefront sensors(SHWFSs)can simultaneously measure amplitude and phase distribution of wavefront with low resolution.The conventional incoherent phase detection methods require two CMOSs placed in different positions,estimating the phase information of wavefront by the exact difference between the diffraction images.This detection system has disadvantages of complexity and uncertainty.In this article,we propose to use a broadband light as the illumination light source,and the single-shot color image is needed as the input of the neural network.Gradient descent algorithm functions is used as the optimizer to complete the phase reconstruction.Both simulation and experimental results show that this technology combines physical models with neural networks to achieve phase reconstruction with high resolution,high speed,low cost.As the mean square error function(MSE)to evaluate the quality of reconstructed images,the minimum value of this method can reach 0.089 rad,and the phase reconstruction quality of transparent cells is 0.133 rad,which is better than the GS method(0.320 rad)and the TIE method(0.378 rad).In consequence,the phase reconstruction method of broadband light source based on neural network can be used in adaptive optics,live cell biological real-time imaging field,etc.