数字全息术
计算机科学
全息术
图像翻译
人工智能
相(物质)
深度学习
计算机视觉
图像(数学)
间断(语言学)
数字成像
生成语法
相位展开
图像处理
数字全息显微术
数字图像
光学
数学
干涉测量
物理
量子力学
数学分析
作者
Seonghwan Park,You-Hyun Kim,Inkyu Moon
摘要
Digital holography can provide quantitative phase images related to the morphology and content of biological samples. After the numerical image reconstruction, the phase values are limited between −π and π; thus, discontinuity may occur due to the modulo 2π operation. We propose a new deep learning model that can automatically reconstruct unwrapped focused-phase images by combining digital holography and a Pix2Pix generative adversarial network (GAN) for image-to-image translation. Compared with numerical phase unwrapping methods, the proposed GAN model overcomes the difficulty of accurate phase unwrapping due to abrupt phase changes and can perform phase unwrapping at a twice faster rate. We show that the proposed model can generalize well to different types of cell images and has high performance compared to recent U-net models. The proposed method can be useful in observing the morphology and movement of biological cells in real-time applications.
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