金子蘅 1,2,3徐可 1,2,3张宁远 1,2,3邓潇 1,2,3[ ... ]冯世杰 1,2,3,*
作者单位
摘要
1 南京理工大学电子工程与光电技术学院智能计算成像实验室,江苏 南京 210094
2 南京理工大学智能计算成像研究院,江苏 南京 210019
3 南京理工大学江苏省光谱成像与智能感知重点实验室,江苏 南京 210094
近年来,深度学习技术广泛应用于计算光学三维成像的研究中。在条纹投影轮廓术中,通过训练深度学习网络,可从单幅条纹图像中恢复高精度的相位信息。然而,为了训练神经网络模型,通常需要耗费大量的时间成本和人力成本来采集训练数据集。为了解决该问题:首先,建立数字孪生条纹投影系统,并利用域随机化技术对虚拟照明光栅进行增强,使用计算机进行虚拟扫描,生成大量仿真光栅条纹图像;其次,利用仿真光栅图像对U-Net神经网络进行预训练;最后,引入迁移学习,采用少量真实光栅条纹图像对神经网络进行参数微调。由于U-Net的结构特殊性,提出并分析了“从左至右”“从上至下”“全局微调”等3种U-Net神经网络微调策略。实验结果表明,采用“从上至下”策略微调U-Net“瓶颈”网络模块的方法可获得最佳的迁移学习结果,神经网络的相位预测精度可得到显著提升。相比于使用大量真实数据进行训练,所述方法仅利用20%的数据就可训练神经网络获得高精度的相位重建结果。
计算成像 条纹投影 深度学习 迁移学习 条纹分析 computational imaging fringe projection deep learning transfer learning fringe analysis 
激光与光电子学进展
2024, 61(2): 0211024
周笑 1,2,3左超 1,2,3,**刘永焘 1,2,3,*
作者单位
摘要
1 南京理工大学电子工程与光电技术学院智能计算成像实验室(SCILab),江苏 南京 210094
2 南京理工大学江苏省光谱成像与智能感知重点实验室,江苏 南京 210094
3 南京理工大学智能计算成像研究院(SCIRI),江苏 南京 210019
随着生物医学研究对复杂组织结构和功能的深入探索,高分辨率、高信噪比的深组织成像技术变得愈加重要。传统的显微镜技术往往局限于二维、透明的生物薄样本的观测,这在很大程度上无法满足当前生物医学领域对三维深组织体成像的研究需求。光片荧光显微镜凭借其低光损伤、高采集速率、大视场、体成像等优点被生物学家广泛使用。然而,生物组织固有的高散射特性仍然为深层成像带来了巨大的挑战。本文重点介绍了光片荧光显微成像技术在深组织成像领域的最新进展,特别是应对高散射样本挑战的解决策略,旨在为相关领域的研究人员提供有价值的参考,助力其对该前沿技术的最新进展和应用前景的理解。首先,阐述了光片荧光显微镜的基本原理和高散射吸收特性的形成原因及影响;然后,进一步阐明了增加组织穿透深度、应对光散射和吸收等问题的最新进展;最后,探讨了具有大穿透深度和强抗散射能力的光片荧光显微成像技术的发展前景以及潜在应用。
荧光显微 光片照明 深组织成像 三维成像 光学散射 fluorescence microscopy light-sheet illumination deep tissue imaging three-dimensional imaging optical scattering 
激光与光电子学进展
2024, 61(2): 0211010
李晟 1,2,3王博文 1,2,3管海涛 1,2,3梁坤瑶 1,2,3[ ... ]左超 1,2,3,**
作者单位
摘要
1 南京理工大学电子工程与光电技术学院,智能计算成像实验室(SCILab),江苏 南京 210094
2 南京理工大学智能计算成像研究院(SCIRI),江苏 南京 210019
3 江苏省光谱成像与智能感知重点实验室,江苏 南京 210094
4 陆军装备部驻南京军事代表处,江苏 南京 210024
光学合成孔径探测 计算成像 超分辨 傅里叶叠层 非相干合成孔径 远场成像 optical synthetic aperture detection computational imaging super resolution Fourier ptychography incoherent synthetic aperture far-field imaging 
光电工程
2023, 50(10): 230090
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
许新傲 1,2,3李艺璇 1,2,3钱佳铭 1,2,3冯世杰 1,2,3左超 1,2,3,*
作者单位
摘要
1 南京理工大学 电子工程与光电技术学院 智能计算成像实验室(SCILab),江苏 南京 210094
2 南京理工大学 智能计算成像研究院(SCIRI),江苏 南京 210019
3 江苏省光谱成像与智能感知重点实验室,江苏 南京 210094
三维形貌测量在先进制造、航空航天、生物医学等领域发挥着重要的应用。凭借高精度、全视场、非接触等优点,条纹投影轮廓术是目前使用最广泛的一种光学三维测量手段。为了获得物体全局三维信息,通常需要将待测物置于转台之上,通过不断地扫描和拼接来获得物体的全局信息。然而,传统的扫描和拼接是以离线的方式进行的,导致整个三维模型的重建速度缓慢。现有的实时点云配准方法虽然能够有效提高点云扫描与拼接的速度,但实时点云拼接的精度依然受待测物的运动状态影响。本文针对上述问题进行优化改进,提出一种基于全局优化的实时高精度模型重建方法。首先,介绍了一种由粗配准到精配准的快速点云配准算法并提出了基于点云法向量约束的点云初始化算法,能够提升粗配准过程中点云初始位姿计算的稳定性与精度。其次,在精配准阶段引入了图优化算法以获得全局点云位姿的最优解,进一步提升了全局点云配准的精度。实验结果表明,所提方法相比于现有实时模型重建方法,能够实现更高精度且稳定的全局点云配准。特别地,针对动态场景中由于抖动等因素引起的被测物体速度突变等情况,本方法依然能够鲁棒地完成三维模型重建,全方位模型重建的精度达84 μm。
条纹投影轮廓术 图优化 实时 三维重建 点云配准 fringe projection profilometry graph optimization real-time 3D reconstruction point cloud registration 
液晶与显示
2023, 38(6): 748
Shijie Feng 1,2,3Yile Xiao 1,2,3Wei Yin 1,2,3Yan Hu 1,2,3[ ... ]Qian Chen 1,2,*
Author Affiliations
Abstract
1 Nanjing University of Science and Technology, Smart Computational Imaging Laboratory, Nanjing, China
2 Nanjing University of Science and Technology, Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, China
3 Smart Computational Imaging Research Institute of Nanjing University of Science and Technology, Nanjing, China
In recent years, there has been tremendous progress in the development of deep-learning-based approaches for optical metrology, which introduce various deep neural networks (DNNs) for many optical metrology tasks, such as fringe analysis, phase unwrapping, and digital image correlation. However, since different DNN models have their own strengths and limitations, it is difficult for a single DNN to make reliable predictions under all possible scenarios. In this work, we introduce ensemble learning into optical metrology, which combines the predictions of multiple DNNs to significantly enhance the accuracy and reduce the generalization error for the task of fringe-pattern analysis. First, several state-of-the-art base models of different architectures are selected. A K-fold average ensemble strategy is developed to train each base model multiple times with different data and calculate the mean prediction within each base model. Next, an adaptive ensemble strategy is presented to further combine the base models by building an extra DNN to fuse the features extracted from these mean predictions in an adaptive and fully automatic way. Experimental results demonstrate that ensemble learning could attain superior performance over state-of-the-art solutions, including both classic and conventional single-DNN-based methods. Our work suggests that by resorting to collective wisdom, ensemble learning offers a simple and effective solution for overcoming generalization challenges and boosts the performance of data-driven optical metrology methods.
optical metrology fringe-pattern analysis deep learning ensemble learning three-dimensional measurement phase retrieval 
Advanced Photonics Nexus
2023, 2(3): 036010
作者单位
摘要
1 北京航空航天大学仪器科学与光电工程学院,北京 100191
2 中国电子科技集团第十一研究所,北京 100015
3 南京理工大学电子工程与光电技术学院,江苏 南京 210094
针对数字光处理(DLP)投影仪投影速度低从而限制结构光三维测量速度的问题,采用具有兆赫兹量级切换速度的LED阵列作为投影光源,提出一种基于高速LED阵列的条纹结构光三维测量方法。具体地,使用高速LED阵列投影二值条纹图案,通过对投影系统的镜头进行轻微离焦从而在被测三维物体表面获得正弦条纹,然后结合相移法和多频外差法对物体三维高度进行解算重建。使用所提实验系统在21000 frame/s的投影速度下对旋转速度为3000 r/min的阶梯物体进行三维测量,系统对动态物体的测量速度达到6000 Hz,测量精度达到0.1 mm,实现了对高速运动物体的三维形貌重建,同时展现出高速LED阵列作为投影光源提升三维测量速度至兆赫兹量级的可行性。
成像系统 三维测量 高速LED 结构光 离焦投影 imaging systems three-dimensional measurement high-speed LED structured light defocus projection 
激光与光电子学进展
2023, 60(8): 0811015
尹维 1,2,3†李明雨 1,2,3†胡岩 1,2,3冯世杰 1,2,3[ ... ]左超 1,2,3,*
作者单位
摘要
1 南京理工大学电子工程与光电技术学院智能计算成像实验室(SCILab),江苏 南京 210094
2 南京理工大学江苏省光谱成像与智能感知重点实验室,江苏 南京 210094
3 南京理工大学智能计算成像研究院(SCIRI),江苏 南京 210019
4 苏州亚博汉智能科技有限公司(Abham),江苏 苏州 215000
散斑投影轮廓术通过投影单幅随机散斑图案编码场景的深度信息,利用散斑匹配技术建立立体图像间的全局对应关系,从而实现单帧3D重建。但由于被测物体表面的复杂反射特性和双相机间存在的视角差异,投影单幅散斑图案无法为整个测量空间中每个像素编码全局唯一的特征,由此带来的误匹配问题导致测量精度较低,难以满足一些工业场景的高精度测量需求。提出一种基于垂直腔面发射激光器(VCSEL)投影阵列的散斑结构光三维成像技术及其传感器设计方法,所研制的三维结构光传感器集成了3个小型化散斑投影模组,投影一组空间位置不同的散斑图案,对被测场景的深度信息进行高效时空编码。提出一种由粗到精的时空散斑相关算法,以提升测量精度,重建复杂物体的精细轮廓。通过精度分析、三维模型扫描、小目标金属零件检测、复杂场景测量等实验证明,所提三维结构光传感器实现了远距离、大视场的高精度三维测量,可潜在应用于零件分拣、机器人码垛等工业场景。
三维成像 立体视觉 光学成像 散斑投影 three-dimention imaging stereo vision optical imaging speckle projection 
激光与光电子学进展
2023, 60(8): 0811014
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

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