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Plug-and-play algorithm for imaging through scattering media under ambient light interference.

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    |May 24, 2023
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    Summary

    This study introduces a plug-and-play (PnP) algorithm for imaging through scattering media without darkroom conditions. The novel method enhances object reconstruction quality under ambient light, improving practical applications.

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    Area of Science:

    • Computational Imaging
    • Optics
    • Image Reconstruction

    Background:

    • Speckle correlation imaging is versatile for imaging through scattering media.
    • Current methods require darkroom conditions, limiting practical use due to ambient light interference.
    • Ambient light degrades speckle contrast and object reconstruction quality.

    Purpose of the Study:

    • To develop a plug-and-play (PnP) algorithm for restoring objects imaged through scattering media in non-darkroom environments.
    • To overcome limitations of ambient light affecting speckle contrast and image quality.
    • To enable practical applications of imaging through scattering media.

    Main Methods:

    • A novel plug-and-play (PnP) algorithm, PnPGAP-FPR, was developed.
    • The algorithm integrates the generalized alternating projection (GAP) optimization framework, Fienup phase retrieval (FPR) method, and a deep learning network (FFDNeT).
    • Experimental validation was performed to demonstrate the algorithm's efficacy.

    Main Results:

    • The PnPGAP-FPR algorithm successfully restored objects through scattering media under non-darkroom conditions.
    • Significant improvements in object reconstruction quality were observed compared to existing methods.
    • The algorithm demonstrated effectiveness and flexible scalability in experimental settings.

    Conclusions:

    • The developed PnP algorithm offers a robust solution for imaging through scattering media in ambient light.
    • This advancement significantly broadens the potential for practical applications of scattering imaging.
    • The method provides a scalable and effective approach for computational imaging challenges.