基于物理增强自相关估计器（PEACE）的激光散斑粒度分布提取
Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)
摘要：
从成像系统中提取关于高度散射表面的定量信息是具有挑战性的，因为散射光的相位在传播时经历多次折叠，从而导致复杂的散斑图案。一个具体的应用是在制药工业中干燥湿粉末，其中量化粒度分布（PSD）是特别感兴趣的。需要在干燥过程中使用非侵入性且实时的监测探头，但没有合适的候选方案。在这份报告中，我们开发了从PSD到散斑图像的理论关系，并描述了一种用于散斑分析的物理增强自相关估计器（PEACE）机器学习算法，以测量粉末表面的PSD。这种方法同时解决了正问题和逆问题，并且具有更高的可解释性，因为机器学习近似器是由物理定律正则化的。
英文摘要：
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.
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