论文标题
高分辨率的单发相移干扰显微镜使用深神经网络进行定量的生物样品成像
High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples
论文作者
论文摘要
白光相位转移干扰显微镜（WL-PSIM）是工业和生物学标本高分辨率定量成像（QPI）的突出技术。但是，在WL-PSIM中，基本上需要具有精确相移的多个干涉图来测量对象的准确相位。在这里，我们提出了使用过滤的白光相移干涉显微镜（F-WL-PSIM）和深神经网络（DNN）进行准确相位测量的单相移干技术。通过训练DNN生成1）四个相移的帧和2）单个干涉图的直接相结合。网络的训练是在两个不同的样品（即光波导和MG63骨肉瘤细胞）上进行的。此外，通过比较从网络生成和实验记录的干涉图中提取的相位图来验证F-WL-PSIM+DNN框架的性能。当前的方法可以使用单个框架为不同的生物医学应用来进一步加强QPI技术，以实现高分辨率相恢复。
White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industrial and biological specimens. However, multiple interferograms with accurate phase-shifts are essentially required in WL-PSIM for measuring the accurate phase of the object. Here, we present single-shot phase-shifting interferometric techniques for accurate phase measurement using filtered white light phase-shifting interference microscopy (F-WL-PSIM) and deep neural network (DNN). The methods are incorporated by training the DNN to generate 1) four phase-shifted frames and 2) direct phase from a single interferogram. The training of network is performed on two different samples i.e., optical waveguide and MG63 osteosarcoma cells. Further, performance of F-WL-PSIM+DNN framework is validated by comparing the phase map extracted from network generated and experimentally recorded interferograms. The current approach can further strengthen QPI techniques for high-resolution phase recovery using a single frame for different biomedical applications.
