Differential interference contrast phase edging net:an all-optical learning system for edge detection of phase objects  

作　　者：李一鸣 李然 陈泉 栾海涛 卢海军 杨晖 顾敏 张启明 Yiming Li;Ran Li;Quan Chen;Haitao Luan;Haijun Lu;Hui Yang;Min Gu;Qiming Zhang(Institute of Photonic Chips,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Nokia Shanghai Bell Co.,Ltd.,Shanghai 201206,China;College of Medical Instruments,Shanghai University of Medicine and Health Sciences,Shanghai 201318,China)

机构地区：[1]Institute of Photonic Chips,University of Shanghai for Science and Technology,Shanghai 200093,China [2]School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China [3]Nokia Shanghai Bell Co.,Ltd.,Shanghai 201206,China [4]College of Medical Instruments,Shanghai University of Medicine and Health Sciences,Shanghai 201318,China

出　　处：《Chinese Optics Letters》2024年第1期21-27,共7页中国光学快报（英文版）

基　　金：supported by the National Key Research and Development Program of China(Nos.2021YFB2802000 and 2022YFB2804301);Shanghai Municipal Science and Technology Major Project,Science and Technology Commission of Shanghai Municipality(No.21DZ1100500);Shanghai Frontiers Science Center Program(2021-2025 No.20);National Natural Science Foundation of China(Nos.61975123 and 12072200);Science and Technology Development Foundation of Pudong New Area(No.PKX2021-D10)。

摘　　要：Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution.In this work,an all-optical diffractive neural network(DPENet)based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed.Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges.Simulation results show that DPENet achieves F-scores of 0.9308(MNIST)and 0.9352(NIST)and enables real-time edge detection of biological cells,achieving an F-score of 0.7462.

关 键 词：diffractive neural network edge detection phase objects 

分 类 号：O436.1[机械工程—光学工程] TP391.41[理学—光学]