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Simultaneous illumination and imaging based on a single multimode fiber.

Zhenyu Ju, Zhenming Yu, Ziyi Meng

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    Summary
    This summary is machine-generated.

    This study introduces a compact dual-function multimode fiber (MMF) imaging system that transmits both light and images through a single fiber. A novel deep learning network reconstructs images, enabling practical endoscopic imaging without external illumination.

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    Area of Science:

    • Optics and Photonics
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Single-core multimode fiber (MMF) is explored for endoscopic imaging.
    • Current MMF imaging systems require a separate light path for illumination, limiting practicality and compactness.
    • Need for integrated illumination and imaging in endoscopic devices.

    Purpose of the Study:

    • To develop a compact, dual-function MMF imaging system capable of simultaneous light transmission and image transfer.
    • To design and validate a deep learning-based network for reconstructing images transmitted through MMF.
    • To demonstrate the feasibility of integrated endoscopic imaging without external illumination.

    Main Methods:

    • Proposed a dual-function MMF imaging system integrating illumination and image transmission within a single fiber.
    • Developed a novel deep learning encoder-decoder network with fully-connected (FC) layers for image reconstruction.
    • Conducted experiments transmitting images through a 1.6 m MMF to evaluate system performance.

    Main Results:

    • The proposed deep learning network outperformed four other networks in image reconstruction accuracy across various datasets.
    • Effective image reconstruction was achieved even using cropped speckle patterns, significantly reducing computational complexity.
    • Demonstrated the cross-domain generalization capability of the developed network.

    Conclusions:

    • The dual-function MMF imaging system offers a practical and compact solution for endoscopic imaging.
    • The novel deep learning network provides robust image reconstruction from MMF transmissions.
    • The system shows significant potential for developing advanced, integrated endoscopic imaging technologies.