基于深度学习的离轴菲涅耳数字全息非线性重构

针对离轴菲涅耳数字全息图,提出基于深度学习的单幅数字全息非线性重构方法 .采用经典的菲涅耳衍射积分模拟数字全息成像以供给网络训练所需样本,利用深度卷积残差神经网络通过学习数字全息图与相关物像之间的非线性数学映射关系实现全息图的物像重构.数值模拟表明,与传统的频率滤波和四步相移技术实现菲涅耳数字全息重构相比,本文提出的方法可直接消除零级像及孪生像,无需条纹物项抽取预处理步骤,且重构的物像具有较高的质量,针对相同记录参考光下不同衍射距离所生成的测试集亦具有较强的稳健性.

Nonlinear Reconstruction for Off-axis Fresnel Digital Holography with Deep Learning

A nonlinear reconstruction method with a single digital hologram using deep learning was proposed for off-axis Fresnel digital hologram. Classic Fresnel diffraction integral is utilized for simulating digital holographic imaging to provide the training samples,and a deep convolution residual neural network is utilized to implement on the object image reconstruction from the recorded hologram,by learning the nonlinear mathematical mapping from the digital hologram to the corresponding object image. The results of numerical simulation experiments show that the method could directly eliminate zero-order images and twin images without fringe pre-processing procedure for extracting object term,compared with the traditional frequency filtering and four-step phase-shift techniques for achieving Fresnel digital holography reconstruction,as well as high quality reconstructed object image. It also has strong robustness to the test dateset generated with different diffraction distances using same recording reference light waveform.