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 …

Superresolution structured illumination microscopy reconstruction algorithms: a review

X Chen, S Zhong, Y Hou, R Cao, W Wang… - Light: Science & …, 2023 - nature.com
Structured illumination microscopy (SIM) has become the standard for next-generation wide-
field microscopy, offering ultrahigh imaging speed, superresolution, a large field-of-view …

Transport of intensity diffraction tomography with non-interferometric synthetic aperture for three-dimensional label-free microscopy

J Li, N Zhou, J Sun, S Zhou, Z Bai, L Lu… - Light: Science & …, 2022 - nature.com
We present a new label-free three-dimensional (3D) microscopy technique, termed transport
of intensity diffraction tomography with non-interferometric synthetic aperture (TIDT-NSA) …

Concept, implementations and applications of Fourier ptychography

G Zheng, C Shen, S Jiang, P Song, C Yang - Nature Reviews Physics, 2021 - nature.com
The competition between resolution and the imaging field of view is a long-standing problem
in traditional imaging systems—they can produce either an image of a small area with fine …

[HTML][HTML] Transport of intensity equation: a tutorial

C Zuo, J Li, J Sun, Y Fan, J Zhang, L Lu… - Optics and Lasers in …, 2020 - Elsevier
When it comes to “phase measurement” or “quantitative phase imaging”, many people will
automatically connect them with “laser” and “interferometry”. Indeed, conventional …

[HTML][HTML] An integrated imaging sensor for aberration-corrected 3D photography

J Wu, Y Guo, C Deng, A Zhang, H Qiao, Z Lu, J Xie… - Nature, 2022 - nature.com
Planar digital image sensors facilitate broad applications in a wide range of areas,,,–, and
the number of pixels has scaled up rapidly in recent years,. However, the practical …

On the use of deep learning for computational imaging

G Barbastathis, A Ozcan, G Situ - Optica, 2019 - opg.optica.org
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

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 …

Phase recovery and holographic image reconstruction using deep learning in neural networks

Y Rivenson, Y Zhang, H Günaydın, D Teng… - Light: Science & …, 2018 - nature.com
Phase recovery from intensity-only measurements forms the heart of coherent imaging
techniques and holography. In this study, we demonstrate that a neural network can learn to …

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 …