直接用傅里叶透射显微测量数据分类，省去重建步骤
Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction
- 用CNN从原始测量序列中直接提取特征进行分类
- 准确率比单张低分辨率图像高12%，且速度快得多
- 通过学习多测量融合，大幅减少数据量和采集时间
傅里叶透射显微术（FPM）可实现大视场高分辨率成像，在医学细胞分类中极具价值。然而，从数十甚至上百次测量中重建高分辨率图像计算成本高昂，尤其在大视场下更为明显。本文研究了跳过重建步骤，直接对FPM测量数据进行图像内容分类的可行性。结果表明，卷积神经网络（CNN）能有效从测量序列中提取有意义信息，分类性能显著优于仅使用单张带限图像（最高提升12%），同时远快于重建高分辨率图像。此外，我们证明通过学习对多个原始测量值进行融合，可在保持分类精度的同时显著降低数据量（从而缩短采集时间）。
原文摘要 · Abstract (English)
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.
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