[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 …

Artificial intelligence-enabled quantitative phase imaging methods for life sciences

J Park, B Bai, DH Ryu, T Liu, C Lee, Y Luo, MJ Lee… - Nature …, 2023 - nature.com
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and
label-free investigation of the physiology and pathology of biological systems. This review …

Lens-free on-chip 3D microscopy based on wavelength-scanning Fourier ptychographic diffraction tomography

X Wu, N Zhou, Y Chen, J Sun, L Lu, Q Chen… - Light: Science & …, 2024 - nature.com
Lens-free on-chip microscopy is a powerful and promising high-throughput computational
microscopy technique due to its unique advantage of creating high-resolution images across …

Quantitative phase imaging based on holography: trends and new perspectives

Z Huang, L Cao - Light: Science & Applications, 2024 - nature.com
Abstract In 1948, Dennis Gabor proposed the concept of holography, providing a pioneering
solution to a quantitative description of the optical wavefront. After 75 years of development …

Quantitative phase imaging: recent advances and expanding potential in biomedicine

TL Nguyen, S Pradeep, RL Judson-Torres, J Reed… - ACS …, 2022 - ACS Publications
Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with
significant opportunities for biomedical applications. QPI uses the natural phase shift of light …

[HTML][HTML] Nano biosensors: properties, applications and electrochemical techniques

X Huang, Y Zhu, E Kianfar - Journal of Materials Research and Technology, 2021 - Elsevier
A sensor is a tool used to directly measure the test compound (analyte) in a sample. Ideally,
such a device is capable of continuous and reversible response and should not damage the …

Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection

J Hu, H Chen, AA Heidari, M Wang, X Zhang… - Knowledge-Based …, 2021 - Elsevier
This research's genesis is in two aspects: first, a guaranteed solution for mitigating the grey
wolf optimizer's (GWO) defect and deficiencies. Second, we provide new open-minding …

On the use of deep learning for phase recovery

K Wang, L Song, C Wang, Z Ren, G Zhao… - Light: Science & …, 2024 - nature.com
Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …

Non-polynomial framework for stress and strain response of the FG-GPLRC disk using three-dimensional refined higher-order theory

MSH Al-Furjan, M Habibi, A Ghabussi, H Safarpour… - Engineering …, 2021 - Elsevier
This article presents a non-polynomial framework for bending responses of functionally
graded-graphene nanoplatelets composite reinforced (FG-GPLRC) disk based upon three …

[HTML][HTML] Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography

Y Gao, L Cao - Light: Advanced Manufacturing, 2023 - light-am.com
Holography provides access to the optical phase. The emerging compressive phase
retrieval approach can achieve in-line holographic imaging beyond the information-theoretic …