同名作者【Yilin He】论文列表 -- 中国光学期刊网

同名作者【Yilin He】论文列表

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Imaging Systems, Microscopy, and Displays

Faster structured illumination microscopy using complementary encoding-based compressive imaging

Zhengqi Huang ¹Yunhua Yao ^1,5,*Yilin He ¹Yu He ¹[ ... ]Shian Zhang ^1,3,4,7,*

Author Affiliations

Abstract

¹ State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China

² School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China

³ Joint Research Center of Light Manipulation Science and Photonic Integrated Chip of East China Normal University and Shandong Normal University, East China Normal University, Shanghai 200241, China

⁴ Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China

⁵ e-mail: yhyao@lps.ecnu.edu.cn

⁶ e-mail: zhywang@uestc.edu.cn

⁷ e-mail: sazhang@phy.ecnu.edu.cn

Structured illumination microscopy (SIM) has been widely applied to investigate intricate biological dynamics due to its outstanding super-resolution imaging speed. Incorporating compressive sensing into SIM brings the possibility to further improve the super-resolution imaging speed. Nevertheless, the recovery of the super-resolution information from the compressed measurement remains challenging in experiments. Here, we report structured illumination microscopy with complementary encoding-based compressive imaging (CECI-SIM) to realize faster super-resolution imaging. Compared to the nine measurements to obtain a super-resolution image in a conventional SIM, CECI-SIM can achieve a super-resolution image by three measurements; therefore, a threefold improvement in the imaging speed can be achieved. This faster imaging ability in CECI-SIM is experimentally verified by observing tubulin and actin in mouse embryonic fibroblast cells. This work provides a feasible solution for high-speed super-resolution imaging, which would bring significant applications in biomedical research.

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Photonics Research

2024, 12(4): 740

Research Articles

Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels

Yu He ^1†Yunhua Yao ¹Yilin He ¹Zhengqi Huang ¹[ ... ]Shian Zhang ^1,5,6,*

Author Affiliations

Abstract

¹ East China Normal University, School of Physics and Electronic Science, State Key Laboratory of Precision Spectroscopy, Shanghai, China

² Shenzhen University, Institute of Microscale Optoelectronics, Nanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Shenzhen, China

³ Peking University, Biomedical Engineering Department, Beijing, China

⁴ Peking University, School of Physics, State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics, Beijing, China

⁵ East China Normal University, Joint Research Center of Light Manipulation Science and Photonic Integrated Chip of East China Normal University and Shandong Normal University, Shanghai, China

⁶ Shanxi University, Collaborative Innovation Center of Extreme Optics, Taiyuan, China

Structured illumination microscopy (SIM) has been widely applied in the superresolution imaging of subcellular dynamics in live cells. Higher spatial resolution is expected for the observation of finer structures. However, further increasing spatial resolution in SIM under the condition of strong background and noise levels remains challenging. Here, we report a method to achieve deep resolution enhancement of SIM by combining an untrained neural network with an alternating direction method of multipliers (ADMM) framework, i.e., ADMM-DRE-SIM. By exploiting the implicit image priors in the neural network and the Hessian prior in the ADMM framework associated with the optical transfer model of SIM, ADMM-DRE-SIM can further realize the spatial frequency extension without the requirement of training datasets. Moreover, an image degradation model containing the convolution with equivalent point spread function of SIM and additional background map is utilized to suppress the strong background while keeping the structure fidelity. Experimental results by imaging tubulins and actins show that ADMM-DRE-SIM can obtain the resolution enhancement by a factor of ∼1.6 compared to conventional SIM, evidencing the promising applications of ADMM-DRE-SIM in superresolution biomedical imaging.

structured illumination microscopy superresolution imaging resolution enhancement untrained neural network

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Advanced Photonics Nexus

2023, 2(4): 046005

Research Articles

Temporal compressive super-resolution microscopy at frame rate of 1200 frames per second and spatial resolution of 100 nm

Download：612次

Yilin He ^1†Yunhua Yao ¹Dalong Qi ¹Yu He ¹[ ... ]Shian Zhang ^1,4,*

Author Affiliations

Abstract

¹ East China Normal University, School of Physics and Electronic Science, State Key Laboratory of Precision Spectroscopy, Shanghai, China

² Shenzhen University, Institute of Microscale Optoelectronics, Nanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology, Shenzhen, China

³ Peking University, School of Physics, Frontiers Science Center for Nanooptoelectronics, State Key Laboratory for Mesoscopic Physics, Beijing, China

⁴ Shanxi University, Collaborative Innovation Center of Extreme Optics, Taiyuan, China

Various super-resolution microscopy techniques have been presented to explore fine structures of biological specimens. However, the super-resolution capability is often achieved at the expense of reducing imaging speed by either point scanning or multiframe computation. The contradiction between spatial resolution and imaging speed seriously hampers the observation of high-speed dynamics of fine structures. To overcome this contradiction, here we propose and demonstrate a temporal compressive super-resolution microscopy (TCSRM) technique. This technique is to merge an enhanced temporal compressive microscopy and a deep-learning-based super-resolution image reconstruction, where the enhanced temporal compressive microscopy is utilized to improve the imaging speed, and the deep-learning-based super-resolution image reconstruction is used to realize the resolution enhancement. The high-speed super-resolution imaging ability of TCSRM with a frame rate of 1200 frames per second (fps) and spatial resolution of 100 nm is experimentally demonstrated by capturing the flowing fluorescent beads in microfluidic chip. Given the outstanding imaging performance with high-speed super-resolution, TCSRM provides a desired tool for the studies of high-speed dynamical behaviors in fine structures, especially in the biomedical field.

super-resolution microscopy high-speed imaging compressive sensing deep learning image reconstruction

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Advanced Photonics

2023, 5(2): 026003

Research Articles

Exploring Femtosecond Laser Ablation by Snapshot Ultrafast Imaging and Molecular Dynamics Simulation

Jiali Yao ¹Dalong Qi ¹Hongtao Liang ¹Yilin He ¹[ ... ]Shian Zhang ^1,2,3,*

Author Affiliations

Abstract

¹ State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China

² Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China

³ Collaborative Innovation Center of Light Manipulations and Applications, Shandong Normal University, Jinan 250358, China

Femtosecond laser ablation (FLA) has been playing a prominent role in precision fabrication of material because of its circumvention of thermal effect and extremely high spatial resolution. Molecular dynamics modeling, as a powerful tool to study the mechanism of femtosecond laser ablation, still lacks the connection between its simulation results and experimental observations at present. Here we combine a single-shot chirped spectral mapping ultrafast photography (CSMUP) technique in experiment and a three-dimensional two-temperature model-based molecular dynamics (3D TTM-MD) method in theory to jointly investigate the FLA process of bulky gold. Our experimental and simulated results show quite high consistency in time-resolved morphologic dynamics. According to the highly accurate simulations, the FLA process of gold at the high laser fluence is dominated by the phase explosion, which shows drastic vaporized cluster eruption and pressure dynamics, while the FLA process at the low laser fluence mainly results from the photomechanical spallation, which shows moderate temperature and pressure dynamics. This study reveals the ultrafast dynamics of gold with different ablation schemes, which has a guiding significance for the applications of FLA on various kinds of materials.

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Ultrafast Science

2022, 2(1): 9754131

Nonlinear Optics

Effect of concentration on the formation time of diffraction rings in spatial self-phase modulation

Yilin He Jingdi Zhang Si Xiao ^*Yingwei Wang ^**Jun He

Author Affiliations

Abstract

Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, Changsha 410083, China

A new unsaturated wind-chime model is proposed for calculating the formation time of the diffraction rings induced by spatial self-phase modulation (SSPM) in molybdenum disulfide suspension. To optimize the traditional wind-chime model, the concentration variable of 2D materials was introduced. The results of the unsaturated wind-chime model match quite well with the SSPM experimental results of molybdenum disulfide. Based on this model, the shortest formation time of diffraction rings and their corresponding concentration and light intensity can be predicted using limited data. Theoretically, by increasing the viscosity coefficient of the solution, the response time of the diffraction ring, to reach the maximum value, can be significantly reduced. It has advanced significance in shortening the response time of photonic diodes.

spatial self-phase modulation wind-chime model nonlinear optics

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Chinese Optics Letters

2022, 20(1): 011901

Letters

Single-shot spectral-volumetric compressed ultrafast photography

Download：774次

Pengpeng Ding ^1†Yunhua Yao ¹Dalong Qi ^1,*Chengshuai Yang ¹[ ... ]Shian Zhang ^1,3,*

Author Affiliations

Abstract

¹ East China Normal University, School of Physics and Electronic Science, State Key Laboratory of Precision Spectroscopy, Shanghai, China

² Institut National de la Recherche Scientifique, Centre Énergie Matériaux Télécommunications, Laboratory of Applied Computational Imaging, Varennes, Québec, Canada

³ Shanxi University, Collaborative Innovation Center of Extreme Optics, Taiyuan, China

In ultrafast optical imaging, it is critical to obtain the spatial structure, temporal evolution, and spectral composition of the object with snapshots in order to better observe and understand unrepeatable or irreversible dynamic scenes. However, so far, there are no ultrafast optical imaging techniques that can simultaneously capture the spatial–temporal–spectral five-dimensional (5D) information of dynamic scenes. To break the limitation of the existing techniques in imaging dimensions, we develop a spectral-volumetric compressed ultrafast photography (SV-CUP) technique. In our SV-CUP, the spatial resolutions in the x, y and z directions are, respectively, 0.39, 0.35, and 3 mm with an 8.8 mm × 6.3 mm field of view, the temporal frame interval is 2 ps, and the spectral frame interval is 1.72 nm. To demonstrate the excellent performance of our SV-CUP in spatial–temporal–spectral 5D imaging, we successfully measure the spectrally resolved photoluminescent dynamics of a 3D mannequin coated with CdSe quantum dots. Our SV-CUP brings unprecedented detection capabilities to dynamic scenes, which has important application prospects in fundamental research and applied science.

ultrafast optical imaging multi-dimensional imaging computational imaging compressed sensing image reconstruction

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Advanced Photonics

2021, 3(4): 045001

DEEP LEARNING IN PHOTONICS

High-fidelity image reconstruction for compressed ultrafast photography via an augmented-Lagrangian and deep-learning hybrid algorithm

Download：682次

Chengshuai Yang ¹Yunhua Yao ^1,6,*Chengzhi Jin ¹Dalong Qi ¹[ ... ]Shian Zhang ^1,4,5,7,*

Author Affiliations

Abstract

¹ State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China

² Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA

³ Institut National de la Recherche Scientifique, Centre Énergie Matériaux Télécommunications, Laboratory of Applied Computational Imaging, Varennes, Québec J3X1S2, Canada

⁴ Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan 030006, China

⁵ Collaborative Innovation Center of Light Manipulations and Applications, Shandong Normal University, Jinan 250358, China

⁶ e-mail: yhyao@lps.ecnu.edu.cn

⁷ e-mail: sazhang@phy.ecnu.edu.cn

Compressed ultrafast photography (CUP) is the fastest single-shot passive ultrafast optical imaging technique, which has shown to be a powerful tool in recording self-luminous or non-repeatable ultrafast phenomena. However, the low fidelity of image reconstruction based on the conventional augmented-Lagrangian (AL) and two-step iterative shrinkage/thresholding (TwIST) algorithms greatly prevents practical applications of CUP, especially for those ultrafast phenomena that need high spatial resolution. Here, we develop a novel AL and deep-learning (DL) hybrid (i.e., $AL + DL$ ) algorithm to realize high-fidelity image reconstruction for CUP. The $AL + DL$ algorithm not only optimizes the sparse domain and relevant iteration parameters via learning the dataset but also simplifies the mathematical architecture, so it greatly improves the image reconstruction accuracy. Our theoretical simulation and experimental results validate the superior performance of the $AL + DL$ algorithm in image fidelity over conventional AL and TwIST algorithms, where the peak signal-to-noise ratio and structural similarity index can be increased at least by 4 dB (9 dB) and 0.1 (0.05) for a complex (simple) dynamic scene, respectively. This study can promote the applications of CUP in related fields, and it will also enable a new strategy for recovering high-dimensional signals from low-dimensional detection.

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Photonics Research

2021, 9(2): 02000B30

Nonlinear Optics

Revealing the intrinsic nonlinear optical response of a single MoS₂ nanosheet in a suspension based on spatial self-phase modulation

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Si Xiao ¹Ying Ma ¹Yilin He ¹Yiduo Wang ¹[ ... ]Yingwei Wang ^1,3,*

Author Affiliations

Abstract

¹ Hunan Key Laboratory for Super-microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha 410083, China

² e-mail: junhe@csu.edu.cn

³ e-mail: wyw1988@csu.edu.cn

The reorientation of 2D materials caused by nonlocal electron coherence is the formation mechanism of 2D material spatial self-phase modulation under laser irradiation, which is widely known as the “wind-chime” model. Here, we present a method that provides strong evidence for the reorientation of 2D-material-induced spatial self-phase modulation. The traditional “wind-chime” model was modified by taking into account the attenuation, i.e., damping of the incident light beam in the direction of the optical path. Accordingly, we can extract the nonlinear refractive index of a single

{MoS}_{2}

nanosheet, instead of simply obtaining the index from an equivalent

{MoS}_{2}

film that was constructed by all nanosheets. Our approach introduces a universal and accurate method to extract intrinsic nonlinear optical parameters from 2D material systems.

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Photonics Research

2020, 8(11): 11001725

Reviews

Single-shot compressed ultrafast photography: a review

Download：1062次

Dalong Qi ¹Shian Zhang ^1,2,*Chengshuai Yang ¹Yilin He ¹[ ... ]Lihong V. Wang ^5,*

Author Affiliations

Abstract

¹ East China Normal University, School of Physics and Electric Science, State Key Laboratory of Precision Spectroscopy, Shanghai, China

² Shanxi University, Collaborative Innovation Center of Extreme Optics, Taiyuan, China

³ University of Illinois at Urbana-Champaign, Department of Electrical and Computer Engineering, Urbana, Illinois, United States

⁴ Institut National de la Recherche Scientifique, Centre Énergie Matériaux Télécommunications, Laboratory of Applied Computational Imaging, Varennes, Québec, Canada

⁵ California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Caltech Optical Imaging Laboratory, Pasadena, California, United States

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields.

ultrafast optical imaging compressed sensing computational imaging single-shot measurement

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Advanced Photonics

2020, 2(1): 014003

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