Real-time noise-free inline self-interference incoherent digital holography with temporal geometric phase multiplexing

Photonics Research, Год журнала: 2023, Номер 11(6), С. 906 - 906

Опубликована: Фев. 27, 2023

In this paper, we propose a real-time incoherent digital holographic (IDH) recording system free from bias and twin-image noises. A motionless three-step polarization-encoded phase-shifter operating at 99 Hz is realized with two electrically controllable birefringence-mode liquid crystal cells in tandem geometric phase lens polarizers. Based on the proposed optical configuration, coaxial straight-line self-interference IDH devised. Notably, elimination of noise three phase-shifted images demonstrated as proof concept. Moreover, complex-valued video acquisitions resolution greater than 20 megapixels are demonstrated, an effective acquisition frequency 33 Hz.

Язык: Английский

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Snapshot Polarization-Sensitive Holography for Detecting Microplastics in Turbid Water

ACS Photonics, Год журнала: 2023, Номер 10(12), С. 4483 - 4493

Опубликована: Ноя. 30, 2023

Microplastic (MP) pollution is a serious environmental problem, which can severely harm the earth's ecosystems and human health. However, in situ characterization of MP particles remains challenging due to complex natural environments such as turbid water. In this work, hybrid computational imaging approach based on holography polarimetry developed for rapid accurate assessment particular, influence scattering media detection experimentally studied. With compact optical configuration an efficient method, system capable seeing through obtaining multimodal information about object snapshot. The results suggest that polarization features substantially improve image contrast even highly addition, it demonstrated properties objects are new discriminative identifying materials. Therefore, portable extremely useful further development monitoring environments.

Язык: Английский

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Generative adversarial neural network for 3D-hologram reconstruction

Laser Physics Letters, Год журнала: 2024, Номер 21(4), С. 045201 - 045201

Опубликована: Фев. 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.

Язык: Английский

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Vision transformer empowered physics-driven deep learning for omnidirectional three-dimensional holography

Optics Express, Год журнала: 2024, Номер 32(8), С. 14394 - 14394

Опубликована: Март 25, 2024

The inter-plane crosstalk and limited axial resolution are two key points that hinder the performance of three-dimensional (3D) holograms. state-of-the-art methods rely on increasing orthogonality cross-sections a 3D object at different depths to lower impact crosstalk. Such strategy either produces unidirectional hologram or induces speckle noise. Recently, learning-based provide new way solve this problem. However, most related works convolution neural networks reconstructed holograms have display quality. In work, we propose vision transformer (ViT) empowered physics-driven deep network which can realize generation omnidirectional Owing global attention mechanism ViT, our CGH has small high resolution. We believe work not only promotes high-quality holographic display, but also opens avenue for complex inverse design in photonics.

Язык: Английский

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Clinical and Biomedical Applications of Lensless Holographic Microscopy

Laser & Photonics Review, Год журнала: 2024, Номер 18(10)

Опубликована: Май 16, 2024

Abstract Many clinical procedures and biomedical research workflows rely on microscopy, including diagnosis of cancer, genetic disorders, autoimmune diseases, infections, quantification cell culture. Despite its widespread use, traditional image acquisition review by trained microscopists is often lengthy expensive, limited to large hospitals or laboratories, precluding use in point‐of‐care settings. In contrast, lensless lensfree holographic microscopy (LHM) inexpensive widely deployable because it can achieve performance comparable expensive bulky objective‐based benchtop microscopes while relying components that cost only a few hundred dollars less. Lab‐on‐a‐chip integration practical enables LHM be combined with single‐cell isolation, sample mixing, in‐incubator imaging. Additionally, many manual tasks conventional are instead computational LHM, focusing, stitching, classification. Furthermore, offers field view hundreds times greater than without sacrificing resolution. Here, the basic principles summarized, as well recent advances artificial intelligence enhanced How applied above applications discussed detail. Finally, emerging applications, high‐impact areas for future research, some current challenges facing adoption identified.

Язык: Английский

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Deep learning in medicine: advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Июль 5, 2024

Язык: Английский

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Advanced Optical Imaging Technologies for Microplastics Identification: Progress and Challenges

Advanced Photonics Research, Год журнала: 2024, Номер 5(11)

Опубликована: Июль 22, 2024

Global concern about microplastic (MP) and nanoplastic (NP) particles is continuously rising with their proliferation worldwide. Effective identification methods for MP NP pollution monitoring are highly needed, but due to different requirements technical challenges, much of the work still in progress. Herein, advanced optical imaging systems that successfully applied or have potential focused on. Compared chemical thermal analyses, unique advantages being nondestructive noncontact allow fast detection without complex sample preprocessing. Furthermore, they capable revealing morphology, anisotropy, material characteristics quick robust detection. This review aims present a comprehensive discussion relevant systems, emphasizing operating principles, strengths, drawbacks. Multiple comparisons analyses among these technologies conducted order provide practical guidelines researchers. In addition, combination other alternative described representative portable devices highlighted. Together, shed light on prospects long‐term environmental protection.

Язык: Английский

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Deep Learning Infrared Holography with Transformer for Crystal Material Characterization

Crystal Growth & Design, Год журнала: 2024, Номер 24(16), С. 6851 - 6864

Опубликована: Авг. 7, 2024

This investigation presents a novel approach for the nondestructive, and real-time analysis of crystalline structures, including transition metal dichalcogenides renowned their optoelectronic capabilities. The methodology employs synergistic blend infrared digital holography deep learning, utilizing an in-line system Transformer-based learning algorithms, to provide detail in material microstructure. article investigates effects different parameters on reproduction fidelity, with particular focus phase accuracy. A holography-guided training strategy is proposed enhance framework's performance. By demonstration applications such as evaluating dielectric characteristics ReS2, detecting thickness layers MoS2, monitoring microstructure evolution during growth NaCl CuSO4 crystals. Not only addresses existing limitations characterization but also offers avenues exploration.

Язык: Английский

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Phase derivative estimation in digital holographic interferometry using a deep learning approach

Applied Optics, Год журнала: 2022, Номер 61(11), С. 3061 - 3061

Опубликована: Март 16, 2022

In digital holographic interferometry, reliable estimation of phase derivatives from the complex interference field signal is an important challenge since these are directly related to displacement a deformed object. this paper, we propose approach based on deep learning for direct in interferometry. Using Y-Net model, our proposed allows simultaneous along vertical and horizontal dimensions. The robustness derivative extraction under both additive white Gaussian noise speckle shown via numerical simulations. Subsequently, demonstrate practical utility method deformation metrology using experimental data obtained

Язык: Английский

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Microplastic pollution assessment with digital holography and zero-shot learning

APL Photonics, Год журнала: 2022, Номер 7(7)

Опубликована: Июнь 15, 2022

Microplastic (MP) pollution poses severe environmental problems. Developing effective imaging tools for the identification and analysis of MPs is a critical step to curtail their proliferation. Digital holographic can record morphological refractive index information such small plastic fragments, yet due heterogeneous sampling environments variations in MP shapes, traditional supervised learning methods are limited use. In this work, we pioneer zero-shot method that combines images with semantic attributes identify samples, even if they have not appeared training dataset. It makes use attention mechanism image feature extraction Kullback–Leibler divergence both alleviate domain shift problem guide mapping function. Experimental results demonstrate effectiveness our approach potential wide variety assessments.

Язык: Английский

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