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    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • Biomedical Engineering

    Background:

    • Single multimode fiber (MMF) ghost imaging shows promise for endoscopy.
    • Practical use is limited by mode coupling and ill-posed reconstruction at low sampling rates.

    Purpose of the Study:

    • To propose a high-fidelity reconstruction framework for MMF ghost imaging.
    • To enhance resolution and robustness for endoscopic applications.

    Main Methods:

    • Physics-Constrained Mamba-UNet incorporating a visual state space model (VSSM).
    • Integration of a differentiable forward physical observation model.
    • Training as a physics-constrained inverse problem.

    Main Results:

    • Achieved 2.2-fold resolution enhancement and high structural similarity.
    • Demonstrated superior performance at sampling rates as low as 5%.
    • Framework shows improved robustness against mode-mixing noise.

    Conclusions:

    • The proposed framework offers a viable pathway for real-time, high-resolution fiber endoscopy.
    • Physics-constrained deep learning effectively addresses challenges in MMF ghost imaging.