Video-Rate Quantitative Phase Imaging Using a Digital Holographic Microscope and a Generative Adversarial Network.
Video-Rate Quantitative Phase Imaging Using a Digital Holographic Microscope and a Generative Adversarial Network.
复制标题
DOI：
10.3390/s21238021
复制
发表时间：
2021-12-01
期刊：
影响因子：
--
通讯作者：
Doblas A
中科院分区：
文献类型：
--
作者：
Castaneda R;Trujillo C;Doblas A
传统的离轴数字全息显微镜(DHM)重建方法依赖于计算处理，包括对样品光谱的空间滤波和干涉波之间的倾斜补偿，以精确地重建生物样品的相位。基于DHM系统的光学配置，可能需要诸如数值聚焦之类的附加计算过程来重建无失真的定量相位图像。无论实现方式如何，任何DHM计算处理都会导致较长的处理时间，从而阻碍DHM用于动态生物过程的视频率渲染。在这项研究中，我们报告了一个条件生成对抗网络(CGAN)，用于在DHM中进行稳健和快速的定量相位成像。由GaN模型提供的重建位相图像提供了稳定的背景水平，增强了不同实验条件下样品的可视化，而传统方法往往在这些条件下失败。使用离轴Mach-Zehnder DHM系统记录的人红细胞对所提出的基于学习的方法进行了训练和验证。经过适当的训练，提出的GaN方法是一种计算效率高的方法，重建DHM图像的速度是传统计算方法的7倍。
The conventional reconstruction method of off-axis digital holographic microscopy (DHM) relies on computational processing that involves spatial filtering of the sample spectrum and tilt compensation between the interfering waves to accurately reconstruct the phase of a biological sample. Additional computational procedures such as numerical focusing may be needed to reconstruct free-of-distortion quantitative phase images based on the optical configuration of the DHM system. Regardless of the implementation, any DHM computational processing leads to long processing times, hampering the use of DHM for video-rate renderings of dynamic biological processes. In this study, we report on a conditional generative adversarial network (cGAN) for robust and fast quantitative phase imaging in DHM. The reconstructed phase images provided by the GAN model present stable background levels, enhancing the visualization of the specimens for different experimental conditions in which the conventional approach often fails. The proposed learning-based method was trained and validated using human red blood cells recorded on an off-axis Mach–Zehnder DHM system. After proper training, the proposed GAN yields a computationally efficient method, reconstructing DHM images seven times faster than conventional computational approaches.
登录
查看更多内容
影响因子：
1.9
作者：
Castaneda, Raul;Garcia-Sucerquia, Jorge
通讯作者：
Garcia-Sucerquia, Jorge
影响因子：
2.4
作者：
Anand, A.;Chhaniwal, V. K.;Javidi, B.
通讯作者：
Javidi, B.
影响因子：
--
作者：
Anand, Arun;Chhaniwal, Vani K.;Javidi, Bahram
通讯作者：
Javidi, Bahram
影响因子：
3.5
作者：
Hayes-Rounds, Charity;Bogue-Jimenez, Brian;Doblas, Ana
通讯作者：
Doblas, Ana
DOI：
10.1364/josaa.23.003177
发表时间：
2006-12-01
影响因子：
1.9
作者：
Colomb, Tristan;Montfort, Frederic;Depeursinge, Christian
通讯作者：
Depeursinge, Christian