基于多角度激发漫反射光信号的浑浊介质光学特性参数识别研究  

Optical Property Parameter Identification of Turbid Media Based on Multi-Angle Excited Diffuse Reflection Light Signal

作　　者：刘宣君 刘丽丽 范可舟 吉训生[2] 郭亚 Liu Xuanjun;Liu Lili;Fan Kezhou;Ji Xunsheng;Guo Ya(Key Laboratory of Aduanced Process Control for Light Industry,Ministry of Education,Jiangnan University,Wuxi 214122,Jiangsu,China;College of IoT Engineering,Jiangman University,Wwxi 214122,Jiangsu,China)

机构地区：[1]江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122 [2]江南大学物联网工程学院,江苏无锡214122

出　　处：《中国激光》2022年第15期101-108,共8页Chinese Journal of Lasers

基　　金：国家自然科学基金面上项目(31771680);国家自然科学基金国际合作项目(51961125102)。

摘　　要：在介质中传播的光的吸收系数、散射系数、各向异性因子和折射率可以用于介质物理与化学特性的检测,因此,这四种参数的反演方法研究非常重要,但目前缺乏能够同时识别这四种参数的算法。针对该问题,提出利用多个角度激发的漫反射光信号增加信息的丰富性,并通过残差神经网络实现浑浊介质吸收系数、散射系数、各向异性和折射率识别的方法。通过蒙特卡罗模型模拟了各种条件下的漫反射光信号,对所提方法进行了验证。在仿真过程中,考虑光纤大小和发散角,并在漫反射光强信号中加入不同等级的噪声以提高网络的泛化能力和抗噪性能。结果表明,当信噪比为40 dB时,所提方法对浑浊介质的吸收系数、散射系数、各向异性因子以及折射率的识别结果的平均相对误差分别为8.6%、4.6%、1.7%和0.9%,验证了所提方法的高精度。Objective The propagation of light in turbid media is affected by the optical parameters of the media including absorption c oefficient(μ),scattering coefficient(μ),isotropic coefficient(g),and refractive index(n).These optical parameters are related to the chemical properties,the internal structures,the physical properties of the media,and the boundary difference and speed of light transmission,including the shape,size,and concentration of different scattering components in the turbid media.By measuring the optical parameters of the turbid media,the material properties,physiological states and pathological changes can be determined,which is very important in various applications including biomedical diagnosis and food safety inspection.However,there is a lack of algorithms that can be simultaneously used to identify these four parameters(μ,μ,g,and n)because the measurement instruments cannot be easily installed.To solve this problem,a method based on a residual neural network is proposed here to determine the four parameters of the turbid media from the diffuse reflection light intensity profiles.Methods First,the diffuse reflection light intensity profiles under different incident excitation light angles are obtained t hrough the Monte Carlo simulation.The incident light spot diameter and the divergence angle are considered in the simulation process.Second,the diffuse light intensities excited under multiple angles are used to enhance the information richness.Third,a residual neural network is used to establish the machine learning mapping model between the diffuse light intensity profiles and the optical parameters of the turbid media,and the prediction of optical parameters is realized.The extracted light intensity values along the long axis are used as the input of the residual neural network,and the output is the optical parameters.Before training and testing,noise is added to the diffuse reflection data in order to simulate the optical measurements under real conditions.The input data is normalized to

关 键 词：生物光学 吸收系数 散射系数 各向异性因子 折射率 光学性能 残差神经网络 

分 类 号：O436.2[机械工程—光学工程]