Open AccessDetermination of material optical properties from diffusive reflection light intensity profiles at multiple distances
Author(s) -
LiLi Liu,
Dan Yin,
Nanyang Zhu,
Jinglu Tan,
Ya Guo
Publication year - 2020
Publication title -
materials research express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.383
H-Index - 35
ISSN - 2053-1591
DOI - 10.1088/2053-1591/ab5ef0
Subject(s) - optics , intensity (physics) , light intensity , monte carlo method , light scattering , refractive index , reflection (computer programming) , isotropy , homogeneity (statistics) , attenuation coefficient , scattering , moment (physics) , computational physics , materials science , physics , mathematics , statistics , computer science , classical mechanics , programming language
Optical absorption and scattering properties are often estimated from the diffusive reflection light intensity at only one distance from the material surface, which often encounters accuracy and convergence issues. In this work, a method was proposed to determine optical properties by using diffusive reflection light intensity profiles at multiple distances, which enhanced data richness as a result of the intensity profiles are linearly independent. In this method, five features of light intensity profiles (contrast, correlation, energy, homogeneity, and second moment) were used to reduce the data dimensions. To demonstrate the effectiveness of the proposed method, Monte Carlo (MC) simulations were used to generate diffusive reflection light intensity profiles with noise at different distances for various combinations of four optical properties (absorption coefficient μ a , scattering coefficient μ s , isotropic coefficient g , and refractive index n ). The five profile feature vectors were used as inputs and the four optical parameters were used as outputs to train and test a backpropagation (BP) neural network. The influences of noise levels and the number of diffusive light intensity profiles on parameter estimation accuracy were investigated. The four optical parameters estimated by the BP network were compared with the results estimated by the traditional least squares method, which shows that the proposed method can estimate the optical properties with higher accuracy and better convergence.