The computational imaging technique of Fourier Ptychographic Microscopy (FPM) enables high-resolution imaging with a wide field of view and can serve as an extremely valuable tool, e.g. in the classification of cells in medical applications. However, reconstructing a high-resolution image from tens or even hundreds of measurements is computationally expensive, particularly for a wide field of view. Therefore, in this paper, we investigate the idea of classifying the image content in the FPM measurements directly without performing a reconstruction step first. We show that Convolutional Neural Networks (CNN) can extract meaningful information from measurement sequences, significantly outperforming the classification on a single band-limited image (up to 12 %) while being significantly more efficient than a reconstruction of a high-resolution image. Furthermore, we demonstrate that a learned multiplexing of several raw measurements allows maintaining the classification accuracy while reducing the amount of data (and consequently also the acquisition time) significantly.

该研究解决了傅里叶相位显微镜（FPM）高分辨率图像重建计算成本高的问题。论文提出了一种新方法，利用卷积神经网络直接从测量序列中进行图像分类，性能比单一带限制图像分类提高了12%。研究表明，学习多路复用多个原始测量能够减少数据量，从而降低采集时间，同时保持分类准确性。

无重建的傅里叶相位显微镜测量直接图像分类