Lensless imaging of pollen grains at three-wavelengths using deep learning
Lensless imaging of pollen grains at three-wavelengths using deep learning
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DOI：
10.1088/2515-7620/aba6d1
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发表时间：
2020-07-01
影响因子：
2.9
通讯作者：
Mills, Ben
中科院分区：
文献类型：
--
作者：
Grant-Jacob, James A.;Praeger, Matthew;Mills, Ben
使用神经网络根据三个激光波长同时照射记录的光散射图案进行花粉粒的图像重建。使用一个网络重建光学图像的形状显示出平均像素精度为 98.9%。另外两个神经网络被证明能够将散射模式转换为 z 堆栈最大强度投影显微镜图像和扫描电子显微镜图像的预测。直接从散射图案产生各种格式的放大图像的能力将适用于一系列领域的粒子传感，包括健康和安全、环境保护、海洋和空间科学。
Image reconstruction of pollen grains was performed using neural networks, from light scattering patterns recorded with simultaneous irradiation at three laser wavelengths. The shapes of the reconstructed optical images using one network were shown to have a pixel accuracy on average of 98.9%. Two other neural networks were shown to be able to convert scattering patterns into predictions of z-stack maximum intensity projection microscope images and scanning electron microscopy images. The capability of producing magnified images in a variety of formats directly from scattering patterns will be applicable to particle sensing in a range of fields, including health and safety, environmental protection, ocean and space science.