Fourier ptychographic microscopy (FPM) is a newly developed technique to capture wide field-of-view (FOV) and high-resolution images that meet the demands of Internet of Things (IoT). FPM enlarges the equivalent numerical aperture of the system and achieves phase imaging by simply employing an angle-varied illumination module. Recently, researches propose to perform the FPM reconstruction with deep learning servers which is costly and requires large datasets. In this paper, we present a new FPM image reconstruction framework termed multi-NNP for Internet of Medical Things (IoMT). Multi-NNP performs multiplex ptychographic reconstruction with a model-based neural network locally rather than on deep learning servers. Our framework simplifies the process and improves the reconstruction performance which promotes the application of wide-field, high-resolution microscopic images in IoMT. Experimental results demonstrate the performance and effectiveness of the proposed framework.

傅里叶气相色谱（FPM）是一项新开发的技术，可捕获满足物联网（IoT）要求的宽视场（FOV）和高分辨率图像。FPM扩大了系统的等效数值孔径，并且只需采用角度可变的照明模块即可实现相位成像。最近，有研究提出使用深度学习服务器执行FPM重建，这很昂贵，并且需要大量数据集。在本文中，我们提出了一种新的FPM图像重建框架，称为用于医学物联网（IoMT）的多NNP。Multi-NNP使用本地基于模型的神经网络（而不是在深度学习服务器上）执行多重笔迹重构。我们的框架简化了流程，提高了重建性能，从而促进了宽视场的应用，IoMT中的高分辨率显微图像。实验结果证明了所提出框架的性能和有效性。