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Efficient target recognition in ghost imaging via optimized 1D convolutional networks at a low sampling ratio.

Ayesha Abbas, Jie Cao, Adeel Rehman

    Applied Optics
    |March 17, 2026
    PubMed
    Summary

    A convolutional neural network (CNN) achieves high accuracy for object recognition in conventional and computational ghost imaging (CGI). This AI model excels in image-free CGI, enabling efficient, low-bandwidth optical sensing with noise resilience.

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    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Object recognition is crucial for imaging systems.
    • Conventional imaging requires high data bandwidth.
    • Computational ghost imaging (CGI) offers a low-bandwidth alternative but faces challenges in feature extraction.

    Purpose of the Study:

    • To demonstrate the efficacy of a convolutional neural network (CNN) for object recognition in both conventional and CGI.
    • To evaluate the CNN's performance in an image-free CGI framework with subsampled data.
    • To assess the CNN's resilience to noise and explore noise-augmented training strategies.

    Main Methods:

    • Training a CNN on the Fashion-MNIST dataset for image classification.
    • Applying the trained CNN to CGI using compressed bucket measurements at an 80% sampling ratio.

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    Last Updated: Mar 19, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

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  • Investigating the CNN architecture's robustness against noise, including noise-augmented training.

    • Main Results:

    • The CNN achieved 92.91% accuracy in conventional image-based classification.
    • In an image-free CGI setting, the CNN attained 97.55% accuracy using only 80% subsampled data.
    • Noise-augmented training significantly improved the CNN's performance in high-noise conditions.

    Conclusions:

    • CNNs are effective for object recognition in both conventional and CGI modalities.
    • The proposed CNN architecture enables efficient, low-bandwidth optical sensing by extracting features from highly subsampled data.
    • The CNN offers a flexible, noise-resilient solution for object recognition, bridging conventional imaging and computational optics.