论文标题
通过基于物理增强的自相关估计器（和平）从激光斑点中提取粒度分布
Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)
论文作者
论文摘要
从成像系统中提取有关高度散射表面的定量信息很具有挑战性，因为散射光的相位在传播时会经历多个折叠，从而产生了复杂的斑点模式。一种特定的应用是制药行业中湿粉末的干燥，其中量化粒度分布（PSD）特别感兴趣。需要在干燥过程中进行非侵入性和实时监测探针，但是为此目的没有合适的候选人。在本报告中，我们开发了从PSD到斑点图像的理论关系，并描述了基于物理增强的自相关估计器（和平）机器学习算法，用于测量粉末表面的PSD。由于机器学习近似值是由物理定律正规化的，因此该方法共同解决了前进和反向问题，并具有增加的解释性。
Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.
