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High-quality ghost imaging through highly complex scattering media with physics-enhanced untrained neural networks.

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    |September 23, 2025
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    Summary

    This study introduces a physics-enhanced untrained neural network (UNN) to improve ghost imaging (GI) through scattering media. The method reconstructs objects effectively even when both light paths are disturbed, overcoming complex optical challenges.

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    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • Machine Learning Applications

    Background:

    • Optical imaging through scattering media is hindered by distorted illumination and detection paths.
    • Ghost imaging (GI) performance degrades due to dynamic scaling factors in complex scattering environments.

    Purpose of the Study:

    • To develop a novel method for high-quality object reconstruction in optical imaging through complex scattering media.
    • To overcome limitations of ghost imaging caused by simultaneous disturbances in illumination and detection paths.

    Main Methods:

    • Utilized a physics-enhanced untrained neural network (UNN) integrated with a ghost imaging (GI) model.
    • Employed rotating ground glass diffusers and a turbidity-varying liquid chamber to create complex scattering.
    • Recorded speckle patterns and single-pixel intensities, with UNN estimating dynamic scaling factors.

    Main Results:

    • Achieved robust and high-quality object reconstruction despite complex and dynamic scattering.
    • Demonstrated effective compensation for beam distortions in both illumination and detection paths.
    • Validated the capability of the physics-enhanced UNN to correct measurements for reliable reconstruction.

    Conclusions:

    • The proposed physics-enhanced UNN method enables robust optical imaging through simultaneously disturbed scattering media.
    • This approach offers a promising solution for overcoming optical scattering challenges in diverse complex scenarios.
    • Opens new avenues for advanced imaging techniques in turbid and dynamic environments.