Structural anisotropy exists prevalently in complex materials. Quantitatively extracting structural information is a long-standing challenge in optical imaging. In this paper, we propose an optimized Discrete Dipole Approximation-Monte Carlo (DDA-MC) algorithm to investigate the polarized light transportation in fibrous materials, which is validated by experiments on polystyrene fibers. Based on this model, the impact of structural anisotropy of fibrous scatterers on polarized light transportation has been analyzed quantitatively. In addition, simulation results show that structural anisotropy like the orientation angle of cylinders can be deduced from polarization signatures of backscattering light distribution with different polarized incidences. Furthermore, as a label-free and non-invasive tool, the Mueller matrix polarimetry has recently demonstrated promising potential in biomedical diagnosis. By combining with the Mueller Matrix Transformation (MMT), we present how the structural characteristics of fibrous materials, including the orientation angle and deviation of fibers, can be obtained by a Mueller matrix parameter. Therefore, this study not only provides an understanding of the role of structural anisotropy in polarized light transport but also showcases new insight into the nondestructive polarization imaging to characterize fibrous materials.

结构各向异性普遍存在于复杂材料中。定量提取结构信息是光学成像中长期存在的挑战。在本文中，我们提出了一种优化的离散偶极子近似-蒙特卡罗 (DDA-MC) 算法来研究纤维材料中的偏振光传输，并通过聚苯乙烯纤维的实验进行验证。基于该模型，定量分析了纤维散射体结构各向异性对偏振光传输的影响。此外，仿真结果表明，可以从具有不同偏振入射的背向散射光分布的偏振特征推导出结构各向异性，如圆柱体的取向角。此外，作为一种无标签和非侵入性的工具，Mueller 矩阵极化法最近在生物医学诊断中显示出有希望的潜力。通过结合穆勒矩阵变换 (MMT)，我们展示了如何通过穆勒矩阵参数获得纤维材料的结构特征，包括纤维的取向角和偏差。因此，这项研究不仅提供了对结构各向异性在偏振光传输中的作用的理解，而且展示了对非破坏性偏振成像表征纤维材料的新见解。