Author Affiliations
Abstract
We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively low-carrier frequency holograms—deep learning assisted variational Hilbert quantitative phase imaging (DL-VHQPI). The method, incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation, reliably and robustly recovers the quantitative phase information of the test objects. It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system. Compared to the conventional end-to-end networks (without a physical model), the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization. The DL-VHQPI is quantitatively studied by numerical simulation. The live-cell experiment is designed to demonstrate the method's practicality in biological research. The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.
quantitative phase imaging digital holography deep learning high-throughput imaging 
Opto-Electronic Science
2023, 2(4): 220023
闵星植 1,2,3段亚轩 1,3,*王拯洲 1,3陈晓义 1,3[ ... ]范尧 1,3
作者单位
摘要
1 中国科学院西安光学精密机械研究所先进光学仪器研究室,陕西 西安 710119
2 中国科学院大学,北京 100049
3 西安市高功率激光测量技术与仪器重点实验室,陕西 西安 710119
为了解决区域法在四波横向剪切干涉波前重构过程中噪声误差沿积分路径累积影响波前重构精度的问题,本文提出了一种路径导引的四波横向剪切干涉波前重构方法。首先分析了噪声环境下无积分路径导引的区域法波前重构存在噪声误差累积的缺陷,然后在此基础上建立了基于差分相位导数偏差的积分路径评价图模型,并给出了基于积分路径导引的波前重构算法流程。为了验证所提方法的有效性,本文进行了理论仿真研究,结果表明在不同信噪比噪声下所提方法能有效地阻止噪声误差的传播和累积。搭建了基于纯相位型液晶空间光调制器的实验验证装置,实验结果表明:所提方法重构波前与理论波前残差的RMS相比无积分路径导引区域法重构波前与理论波前残差的RMS降低了39.7%,且所提方法重构波前PV值与理论波前PV值的偏差相对无积分路径导引区域法重构波前PV值与理论波前PV值的偏差减小了1.6943λ。所提方法可为提高噪声环境下四波横向剪切干涉波前重构精度提供一种有效方法。
测量 波前重构 路径导引 四波横向剪切干涉 差分相位 measurement wavefront reconstruction path-guidance quadri-wave lateral shearing interference differential phase 
中国激光
2023, 50(18): 1804003
Yao Fan 1,2,3Jiasong Sun 1,2,3Yefeng Shu 1,2,3Zeyu Zhang 1,2,3[ ... ]Chao Zuo 1,2,3,5,*
Author Affiliations
Abstract
1 Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technologyhttps://ror.org/01rxvg760, Nanjing 210094, China
2 Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing 210019, China
3 Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China
4 e-mail: chenqian@njust.edu.cn
5 e-mail: zuochao@njust.edu.cn
Quantitative phase imaging (QPI) by differential phase contrast (DPC) with partially coherent illumination provides speckle-free imaging and lateral resolution beyond the coherent diffraction limit, demonstrating great potential in biomedical imaging applications. Generally, DPC employs weak object approximation to linearize the phase-to-intensity image formation, simplifying the solution to the phase retrieval as a two-dimensional deconvolution with the corresponding phase transfer function. Despite its widespread adoption, weak object approximation still lacks a precise and clear definition, suggesting that the accuracy of the QPI results, especially for samples with large phase values, is yet to be verified. In this paper, we analyze the weak object approximation condition quantitatively and explicitly give its strict definition that is applicable to arbitrary samples and illumination apertures. Furthermore, an iterative deconvolution QPI technique based on pseudo-weak object approximation is proposed to overcome the difficulty of applying DPC to large-phase samples without additional data acquisition. Experiments with standard microlens arrays and MCF-7 cells demonstrated that the proposed method can effectively extend DPC beyond weak object approximation to high-precision three-dimensional morphological characterization of large-phase technical and biological samples.
Photonics Research
2023, 11(3): 442
Yefeng Shu 1,2,3Jiasong Sun 1,2,3Jiaming Lyu 4Yao Fan 1,2,3[ ... ]Chao Zuo 1,2,3
Author Affiliations
Abstract
1 Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technolog, 210094, School of Electronic and Optical Engineering, Nanjing Jiangsu Province, People’s Republic of China
2 Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, 210019, 210019, Nanjing Jiangsu Province, People’s Republic of China
3 Jiangsu Key Laboratory of Spectral Imaging Intelligent Sense, 210094, 210094, Nanjing Jiangsu Province, People’s Republic of China
4 Terahertz Technology Innovation Research Institute, University of Shanghai for Science and Technology, University of Shanghai for Science and Technology, 200093 Shanghai, People’s Republic of China
5 School of Computer and Electronic Information, Nanjing Normal University, 210023, Nanjing Normal University, Nanjing Jiangsu Province, People’s Republic of China
6 Department of Biomedical Engineering, University of Connecticut, Storrs, University of Connecticut, Connecticut 06269, USA
PhotoniX
2022, 3(1): 27
Yefeng Shu 1,2,3Jiasong Sun 1,2,3Jiaming Lyu 4Yao Fan 1,2,3[ ... ]Chao Zuo 1,2,3
Author Affiliations
Abstract
1 Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technolog, 210094, School of Electronic and Optical Engineering, Nanjing Jiangsu Province, People’s Republic of China
2 Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, 210019, 210019, Nanjing Jiangsu Province, People’s Republic of China
3 Jiangsu Key Laboratory of Spectral Imaging Intelligent Sense, 210094, 210094, Nanjing Jiangsu Province, People’s Republic of China
4 Terahertz Technology Innovation Research Institute, University of Shanghai for Science and Technology, University of Shanghai for Science and Technology, 200093 Shanghai, People’s Republic of China
5 School of Computer and Electronic Information, Nanjing Normal University, 210023, Nanjing Normal University, Nanjing Jiangsu Province, People’s Republic of China
6 Department of Biomedical Engineering, University of Connecticut, 06269, University of Connecticut, Storrs Connecticut, USA
Quantitative phase imaging (QPI) has emerged as a valuable tool for biomedical research thanks to its unique capabilities for quantifying optical thickness variation of living cells and tissues. Among many QPI methods, Fourier ptychographic microscopy (FPM) allows long-term label-free observation and quantitative analysis of large cell populations without compromising spatial and temporal resolution. However, high spatio-temporal resolution imaging over a long-time scale (from hours to days) remains a critical challenge: optically inhomogeneous structure of biological specimens as well as mechanical perturbations and thermal fluctuations of the microscope body all result in time-varying aberration and focus drifts, significantly degrading the imaging performance for long-term study. Moreover, the aberrations are sample- and environment-dependent, and cannot be compensated by a fixed optical design, thus necessitating rapid dynamic correction in the imaging process. Here, we report an adaptive optical QPI method based on annular illumination FPM. In this method, the annular matched illumination configuration (i.e., the illumination numerical aperture (NA) strictly equals to the objective NA), which is the key for recovering low-frequency phase information, is further utilized for the accurate imaging aberration characterization. By using only 6 low-resolution images captured with 6 different illumination angles matching the NA of a 10x, 0.4 NA objective, we recover high-resolution quantitative phase images (synthetic NA of 0.8) and characterize the aberrations in real time, restoring the optimum resolution of the system adaptively. Applying our method to live-cell imaging, we achieve diffraction-limited performance (full-pitch resolution of $$655\,nm$$ at a wavelength of $$525\,nm$$ ) across a wide field of view ( $$1.77\,mm^2$$ ) over an extended period of time.
PhotoniX
2022, 3(1): 24
作者单位
摘要
南京理工大学 电子工程与光电技术学院,江苏 南京 210094
计算光学显微成像技术将光学编码和计算解码相结合,通过光学操作和图像算法重建来恢复微观物体的多维信息,为显微成像技术突破传统成像能力提供了强大的助力。这项技术的发展得益于现代光学系统、图像传感器以及高性能数据处理设备的优化,同时也被先进的通信技术和设备的发展所赋能。智能手机平台作为高度集成化的电子设备,具有先进的图像传感器和高性能的处理器,可以采集光学系统的图像并运行图像处理算法,为计算光学显微成像技术的实现创造了全新的方式。进一步地,作为可移动通信终端,智能手机平台开放的操作系统和多样的无线网络接入方法,赋予了显微镜灵活智能化操控能力与丰富的显示和处理分析功能,可用于实现各种复杂环境下多样化的生物学检测应用。文中从四个方面综述了基于智能手机平台的计算光学显微成像技术,首先综述了智能手机平台作为光学成像器件的新型显微成像光路设计,接下来介绍了基于智能手机平台先进传感器的计算光学高通量显微成像技术,然后介绍了智能手机平台的数据处理能力和互联能力在计算显微成像中的应用,最后讨论了这项技术现存在的一些问题及解决方向。
智能手机平台 计算光学显微成像 无线传输 即时检验 smartphone platform computational optical microscopy imaging wireless transmission point-of-care testing 
红外与激光工程
2022, 51(2): 20220095
叶燃 1,2徐楚 1汤芬 1尚晴晴 1[ ... ]左超 2,*
作者单位
摘要
1 南京师范大学 计算机与电子信息学院,江苏 南京 210023
2 南京理工大学 电子工程与光电技术学院,江苏 南京 210094
微球超分辨显微成像技术能够突破衍射极限并成倍提高传统光学显微镜的成像分辨率。因其具有成像系统简单,可实时成像,无需荧光染料标记,能在白光照明条件下工作,且可与市场上成熟的显微镜产品相兼容等优点,具有重要研究价值与广阔应用前景,发展潜力巨大。该技术发展至今已取得了众多令人瞩目的研究成果,但现阶段的研究主要集中在微球超分辨成像规律、成像质量的提高、微球的操控方法上。而针对微球透镜的超分辨成像机理与模型,目前尚未形成完善统一的认知与可靠一致的解释。在此背景下,文中梳理归纳了微球透镜近场聚焦及远场成像机理、数学模型、仿真技术等方面的研究工作,分析现有工作的意义与所存在的不足,指出该领域需要着重解决的问题,并对微球成像技术未来的发展方向给予展望。
超分辨成像 光学传递函数 微球 光子纳米射流 成像仿真 super-resolution imaging optical transfer function microsphere photonic nanojet imaging simulation 
红外与激光工程
2022, 51(2): 20220086
Yao Fan 1,2,3,4Jiaji Li 1,2,3,4Linpeng Lu 1,2,3,4Jiasong Sun 1,2,3,4[ ... ]Chao Zuo 1,2,3,4
Author Affiliations
Abstract
1 School of Electronic and Optical Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing, Nanjing University of Science and Technology, Jiangsu Province 210094, China
2 Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing, Nanjing University of Science and Technology, Jiangsu Province 210094, China
3 Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing, Nanjing University of Science and Technology, Jiangsu Province 210094, China
4 Smart Computational Imaging Research Institute (SCRI) of Nanjing University of Science and Technology, Nanjing, Nanjing, Jiangsu Province 210019, China
Computational microscopy, as a subfield of computational imaging, combines optical manipulation and image algorithmic reconstruction to recover multi-dimensional microscopic images or information of micro-objects. In recent years, the revolution in light-emitting diodes (LEDs), low-cost consumer image sensors, modern digital computers, and smartphones provide fertile opportunities for the rapid development of computational microscopy. Consequently, diverse forms of computational microscopy have been invented, including digital holographic microscopy (DHM), transport of intensity equation (TIE), differential phase contrast (DPC) microscopy, lens-free on-chip holography, and Fourier ptychographic microscopy (FPM). These computational microscopy techniques not only provide high-resolution, label-free, quantitative phase imaging capability but also decipher new and advanced biomedical research and industrial applications. Nevertheless, most computational microscopy techniques are still at an early stage of “proof of concept” or “proof of prototype” (based on commercially available microscope platforms). Translating those concepts to stand-alone optical instruments for practical use is an essential step for the promotion and adoption of computational microscopy by the wider bio-medicine, industry, and education community. In this paper, we present four smart computational light microscopes (SCLMs) developed by our laboratory, i.e., smart computational imaging laboratory (SCILab) of Nanjing University of Science and Technology (NJUST), China. These microscopes are empowered by advanced computational microscopy techniques, including digital holography, TIE, DPC, lensless holography, and FPM, which not only enables multi-modal contrast-enhanced observations for unstained specimens, but also can recover their three-dimensional profiles quantitatively. We introduce their basic principles, hardware configurations, reconstruction algorithms, and software design, quantify their imaging performance, and illustrate their typical applications for cell analysis, medical diagnosis, and microlens characterization.
PhotoniX
2021, 2(1): 19
作者单位
摘要
南京理工大学 电子工程与光电技术学院, 江苏 南京 210094
差分相衬(Differential phase contrast, DPC)成像是一种基于部分相干照明调控的无标记非干涉相位成像方法, 它为未染色透明样品提供了一种快速、有效且高分辨率的可视化手段。DPC通过多次非对称照明调控或非对称孔径调制使不可见的样品相位信息转换为成像器件可直接探测的强度信号, 从而为定性相衬成像甚至定量相位重建提供了可能。近年来, 随着该领域研究的逐步深入, 成像的相位传递函数得以明确推导, DPC已经逐步从定性观察走向了定量研究。另一方面, 得益于全孔径照明调控和高效相位反卷积算法, DPC定量相位成像的空间分辨率可达到非相干衍射极限, 并能够获得低噪声、高精度的定量相位重构结果。通过与三维光学传递函数理论交融借鉴, DPC最近已被进一步拓展到了三维衍射层析领域, 实现了厚样品三维折射率的定量成像。文中从DPC成像方法的基本原理、成像系统与算法优化等几个方面对其历史发展、研究现状和最新进展进行了详细综述, 并讨论了该方法现存的一些关键问题以及今后可能的研究方向。
定量相位成像 差分相衬 相位传递函数 衍射层析 quantitative phase imaging differential phase contrast phase transfer function diffraction tomography 
红外与激光工程
2019, 48(6): 0603014
Yao Fan 1,2,3†Jiasong Sun 1,2,3†Qian Chen 1,2,5Xiangpeng Pan 1,2,3[ ... ]Chao Zuo 1,2,3,*
Author Affiliations
Abstract
1 School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2 Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China
3 Smart Computational Imaging (SCI) Laboratory, Nanjing University of Science and Technology, Nanjing 210094, China
4 Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, USA
5 e-mail: chenqian@njust.edu.cn
Differential phase contrast microscopy (DPC) provides high-resolution quantitative phase distribution of thin transparent samples under multi-axis asymmetric illuminations. Typically, illumination in DPC microscopic systems is designed with two-axis half-circle amplitude patterns, which, however, result in a non-isotropic phase contrast transfer function (PTF). Efforts have been made to achieve isotropic DPC by replacing the conventional half-circle illumination aperture with radially asymmetric patterns with three-axis illumination or gradient amplitude patterns with two-axis illumination. Nevertheless, the underlying theoretical mechanism of isotropic PTF has not been explored, and thus, the optimal illumination scheme cannot be determined. Furthermore, the frequency responses of the PTFs under these engineered illuminations have not been fully optimized, leading to suboptimal phase contrast and signal-to-noise ratio for phase reconstruction. In this paper, we provide a rigorous theoretical analysis about the necessary and sufficient conditions for DPC to achieve isotropic PTF. In addition, we derive the optimal illumination scheme to maximize the frequency response for both low and high frequencies (from 0 to 2NAobj) and meanwhile achieve perfectly isotropic PTF with only two-axis intensity measurements. We present the derivation, implementation, simulation, and experimental results demonstrating the superiority of our method over existing illumination schemes in both the phase reconstruction accuracy and noise-robustness.
Photonics Research
2019, 7(8): 08000890

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!