光学
数字全息术
相位成像
全息术
相(物质)
图像质量
数字全息显微术
质量(理念)
相位恢复
相位展开
计算机科学
干涉测量
物理
人工智能
显微镜
傅里叶变换
图像(数学)
量子力学
作者
H. Li,Xianfeng Xu,Ming Xue,Zhanhong Ren
出处
期刊:Applied Optics
[Optica Publishing Group]
日期:2024-06-26
卷期号:63 (28): G63-G63
被引量:1
摘要
Quantitative phase imaging (QPI) technology is widely used in biomedical imaging and other fields because it can realize exact imaging for transparent phase-type samples, which is of great research significance. The complex amplitude distribution of the object wave obtained by phase-shifting digital holography (PSDH) reproduction can provide phase information for QPI, but its existence of phase wrapping and other problems limits its practical application. Although the traditional phase unwrapping algorithm provides a solution, it has problems such as low unwrapping accuracy or long time running. To solve these problems in QPI, a high-quality phase imaging (HQPI) method by PSDH and deep learning (DL) is proposed, where QPI is achieved by extracting the unknown phase shift using a generalized non-iterative phase shift extraction algorithm and unwrapping the wrapped phase by a DL network. Both numerical simulations and optical experiments verify the feasibility of the method. By comparing with the traditional phase unwrapping algorithm, it is demonstrated that the DL unwrapping method has higher unwrapping accuracy and more efficiency. The results show that the method of HQPI is capable of realizing comparatively fast and accurate QPI.
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