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On the recovery conditions for practical ghost imaging with AMP algorithm.

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    Area of Science:

    • Optics
    • Signal Processing
    • Computational Imaging

    Background:

    • Ghost imaging (GI) is a computational imaging technique.
    • The Approximate Message Passing (AMP) algorithm offers efficient signal recovery.
    • Practical GI systems often face challenges with correlated measurement data.

    Purpose of the Study:

    • To adapt the AMP algorithm for practical ghost imaging scenarios.
    • To investigate the impact of spatially correlated speckles on GI performance.
    • To develop a modified sensing matrix for improved AMP-based GI.

    Main Methods:

    • Preconditioning a sensing matrix to meet AMP algorithm assumptions.
    • Analyzing the statistical properties (Laplace distribution) of the modified sensing matrix.
    • Conducting semi-real experiments and real data processing to validate the method.

    Main Results:

    • The modified sensing matrix satisfies AMP assumptions and exhibits heavy-tailed properties.
    • Spatially correlated speckles significantly increase the required number of measurements compared to i.i.d. measurements.
    • The proposed AMP-based GI method is validated through experimental and real-world data.

    Conclusions:

    • Preconditioning enables AMP for practical GI with correlated speckles.
    • Understanding speckle correlation is crucial for efficient GI system design.
    • The findings guide the development of practical GI remote sensing systems.