Hangzhou Dianzi University, School of Automation and Artificial Intelligence, Hangzhou, China.
Boston University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States.
J Biomed Opt. 2021 Apr;26(4). doi: 10.1117/1.JBO.26.4.046501.
Digital holographic microscopy is widely used to get the quantitative phase information of transparent cells.
However, the sample phase is superimposed with aberrations. To quantify the phase information, aberrations need to be fully compensated.
We propose a technique to obtain aberration-free phase imaging, using the derivative-based principal component analysis (dPCA).
With dPCA, almost all aberrations can be extracted and compensated without requirements on background segmentation, making it efficient and convenient.
It solves the problem that the conventional principal component analysis (PCA) algorithm cannot compensate the common but intricate higher order cross-term aberrations, such as astigmatism and coma. Moreover, the dPCA strategy proposed here is not only suitable for aberration compensation but also applicable for other cases where there exist cross-terms that cannot be analyzed with the PCA algorithm.
数字全息显微镜被广泛用于获取透明细胞的定量相位信息。
然而，样品相位叠加了像差。为了定量相位信息，需要完全补偿像差。
我们提出了一种使用基于导数的主成分分析（dPCA）来获得无像差相位成像的技术。
使用 dPCA，可以提取和补偿几乎所有的像差，而无需背景分割的要求，使其高效便捷。
它解决了传统主成分分析（PCA）算法无法补偿常见但复杂的高阶交叉项像差（如像散和彗差）的问题。此外，这里提出的 dPCA 策略不仅适用于像差补偿，也适用于存在无法用 PCA 算法分析的交叉项的其他情况。
