基于卷积神经网络深度学习模型的光场显微三维粒子空间分布重建  

Deep Learning-Based Three-dimensional Spatial Distribution Reconstruction for Light Field Micro-Particle Image Velocimetry with Convolutional Neural Network

作　　者：沈诗宇 李健[1] 顾梦涛 张彪[1] 许传龙[1] Shen Shiyu;Li Jian;Gu Mengtao;Zhang Biao;Xu Chuanlong(National Engineering Research Center of Power Generation Control and Safety,School of Energy and Environment,Southeast University,Nanjing 210096,Jiangsu,China)

机构地区：[1]东南大学大型发电装备安全运行与智能测控国家工程研究中心,江苏南京210096

出　　处：《光学学报》2023年第21期205-216,共12页Acta Optica Sinica

基　　金：国家自然科学基金(51976038,52006036);东南大学“至善青年学者”资助项目(2242022R40037)。

摘　　要：光场显微粒子图像测速技术通过单光场相机即可实现微尺度三维速度场的测量,但单光场相机角度信息有限,导致粒子重建的轴向分辨率低、重建速度慢。基于此,提出一种基于卷积神经网络深度学习模型的光场显微粒子三维空间分布重建方法,以实现粒子三维分布的高分辨率快速重建。首先,根据光场显微成像模型,基于粒子的实际发光特性生成模拟光场图像,进而构建“粒子空间分布-光场图像”数据集;然后,耦合光场显微成像特点,建立卷积神经网络深度学习模型,通过“粒子空间分布-光场图像”数据集对模型进行学习和训练,获得光场显微三维粒子空间分布预测模型,并对预测模型的性能进行评价;最后,测量水平微通道层流流动中的示踪粒子空间分布和三维速度场。模拟和实验结果表明:相比常规的反卷积方法,所提方法的粒子重建轴向分辨率提高79.3%,基本消除了粒子重建的拉伸效应;单张图像重建时间仅为0.243 s,可以满足实时测量的需求。Objective Light field micro-particle image velocimetry(LF-μPIV)can measure the three-dimensional(3D)velocity field of microflow by a single light field camera.The 3D spatial distribution reconstruction of tracer particles is significant in LF-μPIV.Model-based approaches,including refocusing technology and deconvolution method,are conventionally adopted for the reconstruction.However,the refocusing technology ignores the diffraction effect of the microscope and simplifies the microlens as a pinhole,resulting in low lateral resolution and axial positioning accuracy of the reconstructed tracer particle.Although the deconvolution method improves the lateral resolution based on wave optics theory,the axial resolution is still low due to the limited light-receiving angle of the imaging system.Additionally,the laterally shift-variant point spread function lowers the reconstruction efficiency of the deconvolution method.To this end,the data-driven approach,e.g.,deep learning technique,is proposed to achieve the volumetric reconstruction of the tracer particle distribution.Generally,additional high-resolution 3-D imaging devices such as confocal and selective-plane illumination microscopes are required to establish the‘particle spatial distribution-light field image'dataset.However,they are costly and difficult to implement for the dynamic flow process due to their extremely low temporal resolution.We propose a deep learning-based 3D spatial distribution reconstruction for LF-μPIV with convolutional neural networks to rapidly reconstruct particle distribution with high resolution.Methods With the imaging model of tracer particles in a light field microscope based on the wave optics theory,the light field images are formed through numerical simulations based on the actual luminous characteristics of the particles to efficiently establish the“particle spatial distribution-light field image”dataset.Afterward,the sub-aperture images are extracted from the light field image to acquire angle information since the 2D li

关 键 词：图像重建技术 微尺度流动 深度学习 三维粒子场 光场显微粒子图像测速技术 

分 类 号：O436[机械工程—光学工程]