Jaferzadeh Keyvan, Hwang Seung-Hyeon, Moon Inkyu, Javidi Bahram
Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science & Technology, Dalseong-gun, Daegu, 42988, South Korea.
Department of Electrical and Computer Engineering, U-4157, University of Connecticut, Storrs, Connecticut 06269-4157, USA.
Biomed Opt Express. 2019 Jul 31;10(8):4276-4289. doi: 10.1364/BOE.10.004276. eCollection 2019 Aug 1.
Digital propagation of an off-axis hologram can provide the quantitative phase-contrast image if the exact distance between the sensor plane (such as CCD) and the reconstruction plane is correctly provided. In this paper, we present a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best reconstruction distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately predict the propagation distance from a filtered hologram. The result is compared with the conventional automatic focus-evaluation function. Additionally, our approach can be utilized at the single-cell level, which is useful for cell-to-cell depth measurement and cell adherent studies.
如果能正确给出传感器平面（如电荷耦合器件）与重建平面之间的精确距离，离轴全息图的数字传播就能提供定量相衬图像。在本文中，我们提出了一种深度学习卷积神经网络，其顶层为回归层，用于估计最佳重建距离。使用微球珠和红细胞获得的实验结果表明，该方法能够从滤波后的全息图中准确预测传播距离。将结果与传统的自动聚焦评估函数进行了比较。此外，我们的方法可用于单细胞水平，这对于细胞间深度测量和细胞粘附研究很有用。
