Machine learning holography for measuring 3D particle distribution
Machine learning holography for measuring 3D particle distribution
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DOI：
10.1016/j.ces.2020.115830
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发表时间：
2020-11
影响因子：
4.7
通讯作者：
Siyao Shao;K. Mallery;Jiarong Hong
中科院分区：
文献类型：
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作者：
Siyao Shao;K. Mallery;Jiarong Hong
提出了一种基于数字全息的颗粒粒度测量的图像处理方法。所提出的方法使用了一个修改后的U-网络架构与记录的全息图，全息图重建到每个纵向位置，并在纵向方向上的最小强度投影作为输入，以产生输出组成的焦点粒子在每个纵向位置和他们的2D质心。软广义骰子损失用于颗粒尺寸通道和总变差正则化均方误差损失用于2D质心通道。所提出的方法已被评估使用合成，手动标记的实验，和真实的实验全息图。结果表明，我们的方法有更好的性能相比，最先进的非机器学习方法的粒子提取率和定位精度。我们基于学习的方法可以很容易地扩展到其他类型的基于图像的颗粒尺寸测量任务，如阴影成像和散焦成像。
We propose a learning-based image processing method for particle size measurement based on digital holography in this paper. The proposed approach uses a modified U-net architecture with recorded holograms, hologram reconstructed to each longitudinal location, and minimum intensity projection in longitudinal direction as inputs to produce outputs consisting of in-focus particles at each longitudinal location and their 2D centroids. A soft generalized dice loss is used for the particle size channel and a total variation regularized mean squared error loss is employed for the 2D centroids channel. The proposed method has been assessed using synthetic, manually-labeled experimental, and real experimental holograms. The results demonstrate that our approach have better performance in comparison to the state-of-the-art non-machine-learning methods in terms of particle extraction rate and positioning accuracy. Our learning-based approach can be readily extended to other types of image-based particle size measurement tasks such as shadowgraph imaging and defocusing imaging.