[HTML][HTML] Deep learning in optical metrology: a review

C Zuo, J Qian, S Feng, W Yin, Y Li, P Fan… - Light: Science & …, 2022 - nature.com
With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …

Compensation of aberrations in holographic microscopes: main strategies and applications

DG Sirico, L Miccio, Z Wang, P Memmolo, W Xiao… - Applied Physics B, 2022 - Springer
Digital holography is a technique that provides a non-invasive, label-free, quantitative, and
high-resolution imaging employable in biological and science of matter fields, but not only …

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection

T Nguyen, V Bui, V Lam, CB Raub, LC Chang… - Optics express, 2017 - opg.optica.org
We propose a fully automatic technique to obtain aberration free quantitative phase imaging
in digital holographic microscopy (DHM) based on deep learning. The traditional DHM …

Fast phase retrieval in off-axis digital holographic microscopy through deep learning

G Zhang, T Guan, Z Shen, X Wang, T Hu, D Wang… - Optics express, 2018 - opg.optica.org
Traditional digital holographic imaging algorithms need multiple iterations to obtain focused
reconstructed image, which is time-consuming. In terms of phase retrieval, there is also the …

Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning

VK Lam, TC Nguyen, BM Chung… - Cytometry Part …, 2018 - Wiley Online Library
The noninvasive, fast acquisition of quantitative phase maps using digital holographic
microscopy (DHM) allows tracking of rapid cellular motility on transparent substrates. On two …

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

VK Lam, T Nguyen, V Bui, BM Chung… - … of biomedical optics, 2020 - spiedigitallibrary.org
Significance: We introduce an application of machine learning trained on optical phase
features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to …

[HTML][HTML] Automatic compensation of phase aberrations in digital holographic microscopy based on sparse optimization

Z Ren, J Zhao, EY Lam - Apl Photonics, 2019 - pubs.aip.org
In digital holographic microscopy, phase aberrations, which are usually caused by the
imperfections of components and nontelecentric configuration of the optical system, severely …

Simple and flexible phase compensation for digital holographic microscopy with electrically tunable lens

DN Deng, JZ Peng, WJ Qu, Y Wu, XL Liu, WQ He… - Applied Optics, 2017 - opg.optica.org
In a digital holographic microscopy (DHM) system, different microscope objectives (MOs) will
introduce different phase distortions and thus lead to measurement errors. To address this …

Machine learning with optical phase signatures for phenotypic profiling of cell lines

VK Lam, T Nguyen, T Phan, BM Chung… - Cytometry Part …, 2019 - Wiley Online Library
Robust and reproducible profiling of cell lines is essential for phenotypic screening assays.
The goals of this study were to determine robust and reproducible optical phase signatures …

Three-dimensional imaging of distribution of refractive index by parallel phase-shifting digital holography using Abel inversion

T Fukuda, Y Wang, P Xia, Y Awatsuji, T Kakue… - Optics express, 2017 - opg.optica.org
Although digital holography is a powerful technique obtaining a phase image of a
transparent object, the image reconstructed by the technique merely expresses phase …