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Subjective speckle suppression for 3D measurement using one-dimensional numerical filtering.

A W Stark, E Wong, H Babovsky

    Applied Optics
    |December 25, 2019
    PubMed
    Summary

    Numerical 1D filtering effectively reduces coherent noise in laser speckle projection for 3D reconstruction. This method enhances stereo photogrammetry performance without experimental changes, proving most effective with vertical filter orientation.

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    Area of Science:

    • Optics and Photonics
    • Computer Vision and Image Processing
    • Metrology

    Background:

    • Laser speckle projection is vital for 3D reconstruction in stereo photogrammetry.
    • Coherent noise, or subjective speckles, degrades image quality during surface imaging.
    • Existing noise reduction techniques are often time-consuming or reduce light intensity and depth of field.

    Purpose of the Study:

    • To introduce numerical 1D filtering techniques for reducing subjective speckles in laser speckle projection.
    • To enhance the performance of 3D reconstruction without altering experimental setups.
    • To investigate the impact of filter orientation on noise reduction and reconstruction accuracy.

    Main Methods:

    • Development of a model for contrast reduction analysis.

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  • Investigation of the relationship between filter orientation and setup geometry.
  • Implementation and evaluation of 1D filtering through simulations and real-world experiments.

    • Main Results:

    • Numerical 1D filtering significantly reduces coherent noise in laser speckle patterns.
    • Performance improvements in 3D reconstruction were observed, particularly with optimized filter parameters.
    • A vertical filter orientation relative to the setup geometry was found to be generally most effective.

    Conclusions:

    • Numerical 1D filtering offers a practical solution to subjective speckles in laser speckle projection.
    • This approach enhances 3D reconstruction accuracy and efficiency without requiring experimental modifications.
    • Filter orientation is a critical parameter for maximizing noise reduction and reconstruction quality.