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.
View on arXiv@article{agarwal2025_2505.05054,
title={ Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction },
author={ Navya Sonal Agarwal and Jan Philipp Schneider and Kanchana Vaishnavi Gandikota and Syed Muhammad Kazim and John Meshreki and Ivo Ihrke and Michael Moeller },
journal={arXiv preprint arXiv:2505.05054},
year={ 2025 }
}