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    This study introduces an integrated deep learning network for faster, high-quality reconstruction in single-photon-counting compressive imaging. The novel approach improves imaging system performance by training a binary sampling matrix and an enhanced reconstruction network.

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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Machine Learning

    Background:

    • Single-pixel imaging combined with single-photon counting offers ultrahigh sensitivity.
    • Current limitations include long sampling and reconstruction times, hindering high-resolution, real-time applications.

    Purpose of the Study:

    • To develop a deep learning-based compressive sensing method for single-photon-counting imaging.
    • To accelerate sampling and reconstruction processes while maintaining high image quality.

    Main Methods:

    • Proposed an integrated neural network for sampling and reconstruction in single-photon-counting compressive imaging.
    • Utilized a subpixel convolutional layer to mimic compressed sampling and mitigate blocking artifacts.
    • Modified network propagation to train the initial layer as a binary matrix for system implementation.
    • Developed an improved deep-reconstruction network based on the Inception architecture.

    Main Results:

    • The proposed network effectively integrates sampling and reconstruction.
    • The trained binary matrix is applicable to imaging systems.
    • The improved Inception-based reconstruction network demonstrated superior performance.
    • Experimental results confirmed enhanced reconstruction quality compared to existing methods.

    Conclusions:

    • The integrated neural network offers an effective solution for high-resolution, real-time single-photon-counting compressive imaging.
    • The deep learning approach significantly improves reconstruction speed and quality.
    • This method advances the application of photon-counting imaging in demanding scenarios.