Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction

Navya Sonal Agarwal; Jan Philipp Schneider; Kanchana Vaishnavi Gandikota; Syed Muhammad Kazim; John Meshreki; Ivo Ihrke; Michael Moeller

arXiv · 2505.05054

Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction

Navya Sonal Agarwal·Jan Philipp Schneider·Kanchana Vaishnavi Gandikota·Syed Muhammad Kazim·John Meshreki·Ivo Ihrke·Michael Moeller

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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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eess.IV cs.AI cs.CV

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BibTeX RIS

Navya Sonal Agarwal, Jan Philipp Schneider, Kanchana Vaishnavi Gandikota, Syed Muhammad Kazim, John Meshreki, Ivo Ihrke, Michael Moeller. 2025-05-08. Direct Image Classification from Fourier Ptychographic Microscopy Measurements without Reconstruction. https://arxiv.org/abs/2505.05054

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