Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction

• URL: http://arxiv.org/abs/2505.05054v1
• Date: Thu, 08 May 2025 08:46:28 GMT
• Title: Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction
• Authors: Navya Sonal Agarwal, Jan Philipp Schneider, Kanchana Vaishnavi Gandikota, Syed Muhammad Kazim, John Meshreki, Ivo Ihrke, Michael Moeller,
• Abstract summary: Convolutional Neural Networks (CNN) can extract meaningful information from measurement sequences.<n>We demonstrate that a learned multiplexing of several raw measurements allows maintaining the classification accuracy while reducing the amount of data.
• Score: 7.084713296739152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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