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Updated: Nov 8, 2025

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Deep learning early stopping for non-degenerate ghost imaging.

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We developed a deep learning method to speed up quantum ghost imaging. This approach significantly reduces image acquisition time for object recognition, enabling real-time imaging even with few photons.

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Area of Science:

  • Quantum optics
  • Computational imaging
  • Machine learning

Background:

  • Quantum ghost imaging allows non-degenerate imaging but faces slow reconstruction due to photon sparsity.
  • Classical imaging techniques have limitations in certain applications.

Purpose of the Study:

  • To accelerate image reconstruction in quantum ghost imaging using deep learning.
  • To enable efficient object recognition with limited photon detection.

Main Methods:

  • A two-step deep learning approach was proposed: image enhancement via a convolutional auto-encoder and object recognition via a classifier.
  • The method was tested on a non-degenerate ghost imaging setup with varying physical parameters.

Main Results:

  • Achieved a fivefold decrease in image acquisition time.
  • Maintained high recognition confidence even with sparse photon data.

Conclusions:

  • The deep learning approach significantly reduces experimental time for quantum ghost imaging.
  • This advancement is crucial for achieving real-time ghost imaging and few-photon object recognition, particularly for light-sensitive samples.