Deep learning assisted variational Hilbert quantitative phase imaging

作     者：Zhuoshi Li Jiasong Sun Yao Fan Yanbo Jin Qian Shen Maciej Trusiak Maria Cywińska Peng Gao Qian Chen Chao Zuo Zhuoshi Li;Jiasong Sun;Yao Fan;Yanbo Jin;Qian Shen;Maciej Trusiak;Maria Cywińska;Peng Gao;Qian Chen;Chao Zuo

作者机构：Smart Computational Imaging Laboratory(SCILab)School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjing 210094China Smart Computational Imaging Research Institute(SCIRI)of Nanjing University of Science and TechnologyNanjing 210094China Jiangsu Key Laboratory of Spectral Imaging and Intelligent SenseNanjing 210094China Institute of Micromechanics and PhotonicsWarsaw University of Technology8 Sw.A.Boboli St.Warsaw 02-525Poland School of PhysicsXidian UniversityXi'an 710126China 

出 版 物：《Opto-Electronic Science》 (光电科学（英文）)

年 卷 期：2023年第2卷第4期

页      面：1-11页

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] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 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：We are grateful for financial supports from the National Natural Science Foundation of China(61905115,62105151,62175109,U21B2033,62227818) Leading Technology of Jiangsu Basic Research Plan(BK20192003) Youth Foundation of Jiangsu Province(BK20190445,BK20210338) Biomedical Competition Foundation of Jiangsu Province(BE2022847) Key National Industrial Technology Cooperation Foundation of Jiangsu Province(BZ2022039) Fundamental Research Funds for the Central Universities(30920032101) Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201) National Science Center,Poland(2020/37/B/ST7/03629).The authors thank F.Sun for her contribution to this paper in terms of language expression and grammatical correction 

主　　题：quantitative phase imaging digital holography deep learning high-throughput imaging 

摘      要：We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test *** can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic *** to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model *** DL-VHQPI is quantitatively studied by numerical *** live-cell experiment is designed to demonstrate the method s practicality in biological *** proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.