Opinions on imaging and cutting-edge developments in light microscopy for biomedical applications DOI Open Access
Kirti Prakash, Rainer Heintzmann, Uri Manor

et al.

Applied Physics Letters, Journal Year: 2023, Volume and Issue: 123(26)

Published: Dec. 25, 2023

Optical microscopy has revolutionized the field of biology, enabling researchers to explore intricate details biological structures and processes with unprecedented clarity.Over past few decades, significant strides have been made in tailoring optical techniques meet specific needs biologists (Schermelleh et al., 2019;Prakash 2022).From sample preparation hardware designs software requirements, improvements driven by goal enhancing imaging capabilities facilitating quantitative analysis.The papers featured this issue cover a wide range topics, addressing various aspects for bioimaging.From application nonlinear micro-spectroscopy spatial distribution small gold nanoparticles within multicellular organs background-free (Pope 2023), development multiple feedback-based wavefront shaping method retrieve hidden signals (Rumman 2022), utilization artificial intelligence deep learning algorithms enhanced phase recovery inline holography (Galande each study pushes boundaries what is possible microscopy.Other areas focus include dark-field parallel frequency-domain detection molecular affinity kinetics (Xie radioluminescence nanophosphors (Bai mesoTIRF high-resolution large cell populations (Foylan light-sheet volumetric adaptive (Hong 2022;Keomanee-Dizon

Language: Английский

On the use of deep learning for phase recovery DOI Creative Commons
Kaiqiang Wang, Li Song, Chutian Wang

et al.

Light Science & Applications, Journal Year: 2024, Volume and Issue: 13(1)

Published: Jan. 1, 2024

Phase recovery (PR) refers to calculating the phase of light field from its intensity measurements. As exemplified quantitative imaging and coherent diffraction adaptive optics, PR is essential for reconstructing refractive index distribution or topography an object correcting aberration system. In recent years, deep learning (DL), often implemented through neural networks, has provided unprecedented support computational imaging, leading more efficient solutions various problems. this review, we first briefly introduce conventional methods PR. Then, review how DL provides following three stages, namely, pre-processing, in-processing, post-processing. We also used in image processing. Finally, summarize work provide outlook on better use improve reliability efficiency Furthermore, present a live-updating resource ( https://github.com/kqwang/phase-recovery ) readers learn about

Language: Английский

Citations

75
Insights into infrared crystal phase characteristics based on deep learning holography with attention residual network DOI
Haochong Huang, Haichao Huang, Zhiyuan Zheng

et al.

Journal of Materials Chemistry A, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Through a synergistic blend of infrared digital holography and deep learning, we introduce unconventional mechanistic insight, namely the crystal phase.

Language: Английский

Citations

8
End-to-end infrared radiation sensing technique based on holography-guided visual attention network DOI
Yingying Zhai, Haochong Huang, Dexin Sun

et al.

Optics and Lasers in Engineering, Journal Year: 2024, Volume and Issue: 178, P. 108201 - 108201

Published: March 27, 2024

Language: Английский

Citations

11
Generative adversarial neural network for 3D-hologram reconstruction DOI
Semen A. Kiriy, Dmitry A. Rymov, Andrey S. Svistunov

et al.

Laser Physics Letters, Journal Year: 2024, Volume and Issue: 21(4), P. 045201 - 045201

Published: Feb. 14, 2024

Abstract Neural-network-based reconstruction of digital holograms can improve the speed and quality micro- macro-object images, as well reduce noise suppress twin image zero-order. Usually, such methods aim to reconstruct 2D object or amplitude phase distribution. In this paper, we investigated feasibility using a generative adversarial neural network 3D-scenes consisting set cross-sections. The method was tested on computer-generated optically-registered inline holograms. It enabled all layers scene from each hologram. is improved 1.8 times when compared U-Net architecture normalized standard deviation value.

Language: Английский

Citations

9
Deep empirical neural network for optical phase retrieval over a scattering medium DOI Creative Commons
Hanqian Tu, Haotian Liu, Tuqiang Pan

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 5, 2025

Abstract Supervised learning, a popular tool in modern science and technology, thrives on huge amounts of labeled data. Physics-enhanced deep neural networks offer an effective solution to alleviate the data burden by incorporating analytical model that interprets underlying physical processes. However, it completely fails tackling systems without solution, where wave scattering with multiple input output are typical examples. Herein, we propose concept empirical network (DENN) is hybridization model, which enables seeing through opaque medium untrained manner. The DENN does not rely data, all while delivering as high 58% improvement fidelity compared supervised learning using 30000 pairs for achieving same goal optical phase retrieval. might shed new light applications physics, information science, biology, chemistry beyond.

Language: Английский

Citations

1
Physics-driven universal twin-image removal network for digital in-line holographic microscopy DOI Creative Commons
Mikołaj Rogalski, Piotr Arcab, Luiza Stanaszek

et al.

Optics Express, Journal Year: 2023, Volume and Issue: 32(1), P. 742 - 742

Published: Nov. 24, 2023

Digital in-line holographic microscopy (DIHM) enables efficient and cost-effective computational quantitative phase imaging with a large field of view, making it valuable for studying cell motility, migration, bio-microfluidics. However, the quality DIHM reconstructions is compromised by twin-image noise, posing significant challenge. Conventional methods mitigating this noise involve complex hardware setups or time-consuming algorithms often limited effectiveness. In work, we propose UTIRnet, deep learning solution fast, robust, universally applicable suppression, trained exclusively on numerically generated datasets. The availability open-source UTIRnet codes facilitates its implementation in various systems without need extensive experimental training data. Notably, our network ensures consistency reconstruction results input holograms, imparting physics-based foundation enhancing reliability compared to conventional approaches. Experimental verification was conducted among others live neural glial culture migration sensing, which crucial neurodegenerative disease research.

Language: Английский

Citations

18
Single-shot inline holography using a physics-aware diffusion model DOI Creative Commons
Yunping Zhang, Xihui Liu, Edmund Y. Lam

et al.

Optics Express, Journal Year: 2024, Volume and Issue: 32(6), P. 10444 - 10444

Published: Feb. 27, 2024

Among holographic imaging configurations, inline holography excels in its compact design and portability, making it the preferred choice for on-site or field applications with unique requirements. However, effectively reconstruction from a single-shot measurement remains challenge. While several approaches have been proposed, our novel unsupervised algorithm, physics-aware diffusion model digital (PadDH), offers distinct advantages. By seamlessly integrating physical information pre-trained model, PadDH overcomes need training dataset significantly reduces number of parameters involved. Through comprehensive experiments using both synthetic experimental data, we validate capabilities reducing twin-image contamination generating high-quality reconstructions. Our work represents significant advancements by harnessing full potential prior.

Language: Английский

Citations

7
Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-image-free phase retrieval from single-shot hologram DOI Creative Commons
Xuan Tian, Runze Li, Tong Peng

et al.

Opto-Electronic Advances, Journal Year: 2024, Volume and Issue: 7(9), P. 240060 - 240060

Published: Jan. 1, 2024

Language: Английский

Citations

4
Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications DOI Creative Commons
‪Zhehui Wang, Andrew F. T. Leong, A. Dragone

et al.

Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, Journal Year: 2023, Volume and Issue: 1057, P. 168690 - 168690

Published: Sept. 22, 2023

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science other fields. These are fundamental modern technologies applications, such as nuclear fusion energy, advanced manufacturing, communication, green transportation, which often involve one mole more atoms elementary particles, thus challenging compute by using the first principles of quantum physics forward models. One central problems U-RadIT is optimize information yield through, e.g. high-luminosity X-ray sources, efficient detectors, novel methods collect data, large-bandwidth online offline data processing, regulated underlying statistics, computing power. We review highlight recent progress in: (a.) Detectors high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 application-specific circuits (ASICs), digital photon detectors; (b.) modalities phase contrast imaging, diffractive four-dimensional (4D) tracking; (c.) algorithms neural networks machine learning, (d.) Applications ultrafast material XFELs, synchrotrons laser-driven sources. Hardware-centric approaches optimization constrained detector properties, low signal-to-noise ratio, high cost long development cycles critical hardware components ASICs. Interpretation experimental including comparisons models, frequently hindered sparse measurements, model measurement uncertainties, noise. Alternatively, make increasing learning algorithms, implementations compressed sensing. Machine artificial intelligence approaches, refined information, may also contribute significantly interpretation, uncertainty quantification optimization.

Language: Английский

Citations

10
人工智能定量相位成像：从物理到算法再到物理（内封面文章·特邀） DOI

田璇 TIAN Xuan,

费舒全 FEI Shuquan,

李润泽 LI Runze

et al.

Infrared and Laser Engineering, Journal Year: 2025, Volume and Issue: 54(2), P. 20240490 - 20240490

Published: Jan. 1, 2025

Citations

0