Dynamic quantitative phase imaging based on Ynet-ConvLSTM neural network

被引：10

作者：

Lu, Shengyu ^{[1
]}

Tian, Yong ^{[1
]}

Zhang, Qinnan ^{[1
]}

Lu, Xiaoxu ^{[3
]}

Tian, Jindong ^{[1
,2
]}

机构：

[1] Shenzhen Univ, Coll Phys & Optoelect Engn, Shenzhen 518060, Peoples R China

[2] Shenzhen Univ, Guangdong Lab Artificial Intelligence & Digital E, Shenzhen 518060, Peoples R China

[3] South China Normal Univ, Guangdong Prov Key Lab Nanophoton Funct Mat & Dev, Guangzhou 510006, Peoples R China

来源：

OPTICS AND LASERS IN ENGINEERING | 2022年 / 150卷

基金：

中国国家自然科学基金;

关键词：

Dynamic quantitative phase imaging; Convolutional long short-term memory neural network; Phase image reconstruction; DIGITAL HOLOGRAPHIC MICROSCOPY; SINGLE-SHOT; ALGORITHM; INTERFEROMETRY;

D O I：

10.1016/j.optlaseng.2021.106833

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Dynamic quantitative phase imaging provides an effective solution for measuring the dynamic process of time-varying objects, such as biological samples, fluids, and flexible materials. However, there have been no effective approaches considering the spatial-temporal information of the dynamic process. Here, we report Ynet convolutional long short-term memory (Ynet-ConvLSTM) neural network; it learns the spatial features of the measured object that changes continuously along the time axis of a dynamic process by exploiting the known information of the interferogram sequence and phase images. According to our results, Ynet-ConvLSTM network improved the accuracy of phase image reconstruction in different dynamic circumstances.

引用

页数：14

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