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

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.

Resources

[pdf] [arxiv]

Bibtex

@misc{agarwal2025directimageclassificationfourier,
  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},
  year={2025},
  eprint={2505.05054},
  archivePrefix={arXiv},
  primaryClass={eess.IV},
  url={https://arxiv.org/abs/2505.05054},  }