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Related Experiment Video

Updated: Aug 10, 2025

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals
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Multimode fiber-based greyscale image projector enabled by neural networks with high generalization ability.

Jian Wang, Guangchao Zhong, Daixuan Wu

    Optics Express
    |February 14, 2023
    PubMed
    Summary

    This study enhances image projection through multimode fibers (MMFs) by improving generalization to grayscale images. A novel projector network successfully transmitted unseen binary and grayscale images with high fidelity.

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    Area of Science:

    • Optics
    • Machine Learning
    • Image Transmission

    Background:

    • Multimode fibers (MMFs) offer potential for image delivery but suffer from mode coupling, hindering direct 2D image projection.
    • Previous work utilized Actor-net and Model-net for high-fidelity image projection through MMFs.

    Purpose of the Study:

    • To enhance the generalization capability of image projection systems for grayscale images transmitted through MMFs.
    • To develop a modified projector network capable of handling diverse image types and complexities.

    Main Methods:

    • A modified projector network comprising three subnetworks: forward-net, backward-net, and holography-net, was developed.
    • The network was trained using randomly generated phase maps and corresponding speckle images from a 1-meter MMF.

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  • Experimental validation involved projecting unseen binary (MNIST, EMNIST) and grayscale (Fashion-MNIST) images.

    • Main Results:

    • The trained network successfully projected binary and grayscale images through the MMF.
    • High average Pearson's correlation coefficients were achieved: 0.91 for MNIST, 0.92 for EMNIST, and 0.87 for Fashion-MNIST.
    • The system demonstrated strong generalization ability, projecting images it had not encountered during training.

    Conclusions:

    • The developed projector network significantly improves grayscale image projection fidelity through MMFs.
    • The approach confirms robust generalization capabilities for unseen images, paving the way for advanced MMF-based imaging applications.