A single-frame deep learning phase retrieval algorithm based on defocus grating

被引：0
|
作者：
Qiu X. [1 ,2 ,3 ]
Zhao W. [1 ,2 ]
Yang C. [1 ,2 ,3 ]
Cheng T. [1 ,2 ,3 ]
Wang S. [1 ,2 ]
Xu B. [1 ,2 ]
机构：
[1] Key Laboratory of Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu
[2] Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu
[3] University of Chinese Academy of Sciences, Beijing
来源：
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2020年 / 49卷 / 10期
关键词：
Convolutional neural network; Defocus grating; Phase diversity; Wavefront reconstruction;
D O I：
10.3788/IRLA20200273
中图分类号：
学科分类号：
摘要：
Aiming at drawbacks of slow convergence rate and multiple measuring on focal or defocus plane by CCD in phase diversity algorithm, a single-frame deep learning phase retrieval algorithm based on defocus grating was proposed. Algorithm used a defocus grating to modulate incident wavefront, far-field intensity distribution of focal and positive/negative defocus plane can be acquired on focal plane of lens at the same time. In addition, convergence rate was improved when algorithm applied CNN to replace multiple perturbation optimization process. Numerical simulations indicate that the proposed method can achieve precise high-speed wavefront reconstruction with a single far-field intensity distribution, root mean square (RMS) of residual wavefront is 6.7% of that of incident wavefront, computing time for algorithm to perform wavefront reconstruction can be less than 0.6 ms. Copyright ©2020 Infrared and Laser Engineering. All rights reserved.
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