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    This study introduces a novel convolutional neural network (CNN) method for reconstructing particle fields in synthetic aperture particle image velocimetry (SAPIV). The technique enhances 3D flow measurements under non-uniform illumination.

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    Area of Science:

    • Fluid dynamics
    • Optical measurement techniques

    Background:

    • Synthetic aperture particle image velocimetry (SAPIV) offers high-resolution 3D flow field analysis.
    • Existing intensity-threshold methods struggle with non-uniform illumination.

    Purpose of the Study:

    • To develop a robust particle field reconstruction method for SAPIV in non-uniformly illuminated flows.
    • To improve the accuracy and applicability of 3D flow measurements.

    Main Methods:

    • Investigated focused particle characteristics in re-projected image stacks.
    • Developed a convolutional neural network (CNN) for particle field reconstruction.
    • Utilized structural similarity to extract in-focus particles.

    Main Results:

    • The CNN accurately determines the likelihood of focused particles in 3D image stacks.
    • The method successfully extracts in-focus particles by comparing projected and captured images.
    • Demonstrated remarkable performance in synthetic simulations and experiments.

    Conclusions:

    • The proposed CNN-based method effectively reconstructs particle fields in non-uniformly illuminated conditions.
    • This technique significantly advances SAPIV for 3D velocity measurements in challenging environments.