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Deep learning-based method for non-uniform motion-induced error reduction in dynamic microscopic 3D shape

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    |October 14, 2022
    PubMed
    Summary
    This summary is machine-generated.

    A new deep learning (DL) method reduces errors in dynamic fringe projection profilometry caused by non-uniform motion. This approach effectively compensates for phase errors and establishes nonlinear mappings for accurate 3D measurements.

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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Dynamic fringe projection profilometry is crucial for 3D measurements but suffers from non-uniform motion-induced errors.
    • Deep learning (DL) has shown promise in solving complex nonlinear optical problems.

    Purpose of the Study:

    • To develop a DL-based method for reducing non-uniform motion-induced errors in dynamic fringe projection profilometry.
    • To leverage nonlinear fitting capabilities of DL for enhanced measurement accuracy.

    Main Methods:

    • A specialized dataset incorporating complex nonlinearity was generated for network training.
    • A DL architecture featuring a fringe compensation module for pre-processing was proposed.
    • A deep neural network was utilized to extract error features and create a pixel-wise nonlinear mapping.

    Main Results:

    • The proposed DL method effectively reduces motion-induced errors in fringe projection profilometry.
    • Simulations and real-world experiments validated the method's feasibility for dynamic macroscopic measurements.
    • The approach demonstrated accurate 3D reconstruction in the presence of complex motion.

    Conclusions:

    • The developed DL-based method offers a robust solution for error reduction in dynamic fringe projection profilometry.
    • This technique enhances the accuracy and reliability of 3D measurements under non-uniform motion conditions.
    • The study highlights the potential of DL in addressing complex challenges in optical metrology.