Machine-learning-based high-speed lensless large-field holographic projection using double-sampling Fresnel diffraction method  

作　　者：Chentianfei Shen Tong Shen Qi Chen Qinghan Zhang Jihong Zheng 沈陈天飞;申桐;陈祺;张磬瀚;郑继红(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200433,China)

机构地区：[1]School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200A33,China

出　　处：《Chinese Optics Letters》2022年第5期1-6,共6页中国光学快报（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(No.61975122);the National Key Research and Development Program of China(No.2018YFA0701802).

摘　　要：Machine learning can effectively accelerate the runtime of a computer-generated hologram.However,the angular spectrum method and single fast Fresnel transform-based machine learning acceleration algorithms are still limited in the field-of-view angle of projection.In this paper,we propose an efficient method for the fast generation of large field-of-view holograms combining stochastic gradient descent(SGD),neural networks,and double-sampling Fresnel diffraction(DSFD).Compared with the traditional Gerchberg-Saxton(GS)algorithm,the DSFD-SGD algorithm has better reconstruction quality.Our neural network can be automatically trained in an unsupervised manner with a training set of target images without labels,and its combination with the DSFD can improve the optimization speed significantly.The proposed DSFD-Net method can generate 2000-resolution holograms in 0.05 s.The feasibility of the proposed method is demonstrated with simulations and experiments.

关 键 词：computer-generated hologram holographic display machine learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]