Simplifying the detection of optical distortions by machine learning
作者机构：State Key Laboratory of Modern Optical Instrumentation Department of Neurobiology of the First Affiliated Hospital Zhejiang University School of Medicine Hangzhou 310027P.R.China College of Optical Science and Engineering Zhejiang University Hangzhou 310027P.R.China Center for Neuroscience Department of Neurobiology NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University School of Medicine Hangzhou 310058P.R.China
出 版 物：《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志（英文）)
年 卷 期：2020年第3卷第3期
页 面：61-69页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China(31571110,61735016,81771877) the Natural Science Foundation of Zhejiang Province of China(LZ17F050001) Zhe-jiang Lab(2018EB0ZX01) the Fundamental Research Funds for the Central Universities
主 题：Machine learning adaptive optics wavefront sensor
摘 要：Adaptive optics has been widely used in biological science to recover high-resolution optical image deep into the tissue,where optical distortion detection with high speed and accuracy is strongly ***,we introduce convolutional neural networks,one of the most popular machine learning models,into Shack-Hartmann wavefront sensor(SHWS)to simplify optical distortion detection *** image segmentation or centroid positioning algorithm,the trained network could estimate up to 36th Zernike mode coefficients directly from a full SHWS image within 1.227ms on a personal computer,and achieves prediction accuracy up to 97.4%.The simulation results show that the average root mean squared error in phase residuals of our method is 75.64%lower than that with the modal-based SHWS *** the high detection accuracy and simplified detection processes,this work has the potential to be applied in wavefront sensor-based adaptive optics for in vivo deep tissue imaging.
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