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    Area of Science:

    • Optics and Material Science
    • Artificial Intelligence and Machine Learning

    Background:

    • Ghost imaging (GI) offers a unique approach for material defect detection.
    • Deep learning accelerates GI, but lacks established methods for parameter optimization.
    • Optical system point spread function (PSF) significantly impacts imaging accuracy.

    Purpose of the Study:

    • To investigate the use of convolutional neural networks (CNNs) for correcting PSF effects in GI.
    • To determine optimal model parameters for deep learning-enhanced GI.
    • To improve the accuracy of material defect identification using GI.

    Main Methods:

    • Developed a simplified CNN model with varying kernel sizes.
    • Evaluated model accuracy using data affected by different PSFs.
    • Conducted numerical analysis and empirical experiments.

    Main Results:

    • CNNs effectively correct for PSF effects in GI.
    • Accuracy of defect identification improved substantially when kernel size matched the optical PSF.
    • The proposed method demonstrates practical applicability for material inspection.

    Conclusions:

    • CNNs are a viable tool for enhancing GI performance.
    • Optimizing model parameters, specifically kernel size, is crucial for accurate defect detection.
    • This research provides a pathway for more precise material defect analysis using deep learning-integrated GI.