Center for Biofluid and Biomimic Research, Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea.
Flow Physics and Engineering Laboratory, Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea.
Sci Rep. 2020 Jun 2;10(1):8977. doi: 10.1038/s41598-020-65716-4.
Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3-5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.
数字全息显微镜能够记录包含三维体积信息的样本全息图。然而，为了产生衍射和干涉，需要额外的光学元件，如部分或完全相干光源和针孔。在这里，我们提出了一种基于生成对抗网络（GAN）的深度学习网络，用于将从普通白光光源获取的离焦明场（BF）图像转换为全息图像。通过使用混合 BF 和全息成像技术，收集了 11050 对用于图像转换的训练图像对。通过比较微球和红细胞在 3D 中分布的生成和真实全息图来评估训练后的网络的性能。通过训练后的 GAN 从 BF 图像生成的全息图显示出增强的图像对比度，与真实全息图相比，信噪比提高了 3-5 倍，并且提供了样本的 3D 位置信息和光散射模式。所开发的基于 GAN 的方法是一种有前途的方法，可用于对微尺度物体进行动态分析，即使利用传统的 BF 显微镜设置，也可以精确地提供详细的 3D 位置信息并监测生物样本。
