# Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction Source: [Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:28:46.355Z Authors: Navya Sonal Agarwal, Jan Philipp Schneider, Kanchana Vaishnavi Gandikota, Syed Muhammad Kazim, John Meshreki, Ivo Ihrke, Michael Moeller Published: 2025-05-08T08:46:28.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()