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Updated: Jan 19, 2026

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Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array.

Ulas Kürüm, Peter R Wiecha, Rebecca French

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    This summary is machine-generated.

    Deep learning rapidly reconstructs spectra from speckle patterns. This artificial neural network approach offers faster, more robust hyperspectral imaging than traditional methods.

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    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Spectroscopy

    Background:

    • Speckle patterns contain rich spectral information.
    • Traditional spectral deconstruction methods can be slow and computationally intensive.
    • Developing faster, more robust methods for spectral analysis is crucial.

    Purpose of the Study:

    • To demonstrate deep learning for rapid spectral deconstruction of speckle patterns.
    • To compare deep learning performance against analytical and compressive sensing methods.
    • To achieve real-time hyperspectral information recovery.

    Main Methods:

    • Training artificial neural networks using numerically constructed multispectral datasets.
    • Utilizing a measured spectral transmission matrix for dataset generation.
    • Employing a multi-core, multi-mode fiber array as a random scattering medium.

    Main Results:

    • Reliable reconstruction of discrete and continuous spectra from monochromatic images.
    • Deep learning shows favorable characteristics in oversampling and compressive regimes.
    • Demonstrated real-time recovery of hyperspectral information.

    Conclusions:

    • Deep learning offers significant advantages in speed and robustness for spectral deconstruction.
    • The approach is effective even with sparse undersampling.
    • This method enables real-time hyperspectral imaging applications.