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Adaptive double sinusoidal-assisted EMD 3D shape profilometry measurement method.

Xin Lai, Yunke Xiao, Siqi Tong

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    This study introduces an adaptive algorithm to improve 3D reconstruction accuracy with Fourier transform profilometry under uneven lighting. The method effectively filters noise and background, enhancing shape recovery.

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    Area of Science:

    • Optical Metrology
    • 3D Imaging
    • Signal Processing

    Background:

    • Fourier Transform Profilometry (FTP) is crucial for 3D reconstruction.
    • Non-uniform lighting conditions degrade FTP accuracy.
    • Existing methods struggle with noise and background in complex illumination.

    Purpose of the Study:

    • To enhance 3D reconstruction accuracy in non-uniform light intensity environments.
    • To develop a robust algorithm for Fourier Transform Profilometry.
    • To mitigate the impact of noise and background on fringe patterns.

    Main Methods:

    • Proposed an adaptive double sinusoidal-assisted empirical mode decomposition (ADSAEMD) algorithm.
    • Employed adaptive filtering for background pre-processing.
    • Utilized empirical mode decomposition (EMD) for image mode decomposition, generating intrinsic modal functions (IMFs) to avoid mode mixing problem (MMP).

    Main Results:

    • Successfully separated random noise and background components from fringe patterns.
    • Effectively removed the influence of non-uniform ambient light.
    • Demonstrated accurate 3D shape recovery in simulations and experiments.

    Conclusions:

    • The ADSAEMD algorithm significantly improves 3D reconstruction accuracy under challenging lighting.
    • The method offers a robust solution for noise and background removal in FTP.
    • This technique enhances the reliability of 3D shape measurement in real-world applications.