基于离焦光栅的单帧深度学习相位反演算法  

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

作　　者：邱学晶 赵旺 杨超 程涛 王帅[1,2] 许冰[1,2] Qiu Xuejing;Zhao Wang;Yang Chao;Cheng Tao;Wang Shuai;Xu Bing(Key Laboratory of Adaptive Optics,Institute of Optics and Electronics,Chinese Academy of Sciences,Chengdu 610209,China;Institute of Optics and Electronics,Chinese Academy of Sciences,Chengdu 610209,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院光电技术研究所自适应光学重点实验室,四川成都610209 [2]中国科学院光电技术研究所,四川成都610209 [3]中国科学院大学,北京100049

出　　处：《红外与激光工程》2020年第10期11-18,共8页Infrared and Laser Engineering

基　　金：国家自然科学基金(61805251,61875203,11704382)。

摘　　要：针对目前相位差法收敛速度慢以及需要CCD在焦面以及离焦面多次测量的问题,提出了基于离焦光栅的单帧深度学习相位反演算法。该算法用离焦光栅对入射波前进行调制,可同时在透镜焦平面上获得正负离焦以及焦面远场光强分布;此外算法引入卷积神经网络替代原有的多次扰动寻优过程,波前复原算法耗时大大降低。仿真结果表明:算法可根据单帧透镜焦面远场光强分布实现高精度快速波前复原,残差波前的均方根为入射波前均方根的6.7%,算法进行一次波前复原所需时间可小于0.6 ms。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.

关 键 词：波前复原 离焦光栅 卷积神经网络 相位差法 

分 类 号：O439[机械工程—光学工程]