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    Area of Science:

    • Metrology and Measurement Science
    • Computer Vision and Image Processing
    • Machine Learning Applications

    Background:

    • Microscopic fringe projection profilometry (MFPP) is crucial for 3D measurement, but its precision relies heavily on accurate calibration.
    • Shallow depth of field in MFPP often leads to low-quality target images, causing inaccurate feature detection and calibration parameter estimation.
    • Existing calibration methods struggle with image degradation, limiting overall measurement accuracy.

    Purpose of the Study:

    • To develop an unsupervised learning-based calibration method for MFPP that is robust to image defocus and noise.
    • To enhance image quality and improve calibration accuracy in MFPP systems.
    • To achieve superior performance in both calibration accuracy and measurement precision.

    Main Methods:

    • An unsupervised image deblurring network (UIDNet) was developed to restore sharp target images from degraded inputs.
    • The UIDNet utilizes a multi-quality target dataset, avoiding paired image capture or simulation for more accurate feature learning.
    • Multi-perceptual loss and Fourier frequency loss were integrated into UIDNet for enhanced training.
    • A robust calibration compensation strategy employing 2D discrete Fourier transform was implemented to assess image quality and refine feature center detection.

    Main Results:

    • The proposed unsupervised learning approach significantly improves image quality, recovering sharp target images from defocused and noisy inputs.
    • The method enhances the accuracy of reference feature center detection, crucial for precise calibration.
    • Experimental results demonstrate superior performance in calibration accuracy compared to traditional methods.
    • The improved calibration directly translates to enhanced measurement precision in MFPP systems.

    Conclusions:

    • The unsupervised learning-based calibration method effectively addresses image quality issues in MFPP, particularly defocus and noise.
    • The integration of UIDNet with multi-perceptual and Fourier frequency losses optimizes the deblurring process.
    • The developed strategy provides a robust solution for accurate feature detection and calibration, leading to higher measurement precision.
    • This approach offers a significant advancement for reliable and accurate 3D measurements using MFPP technology.