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Improved phase-to-height mapping method combine with device attitude.

Shuhuan Han1,2, Yanxi Yang3,4, Xinjie Li1

  • 1School of Automation and Information, Xi'an University of Technology, Xi'an, 710048, China.

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|April 26, 2024
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Summary

This study introduces an improved phase-to-height mapping method for 3D phase measurement profilometry. The new approach enhances accuracy and reduces computational needs by combining generalized regression neural networks with device attitude compensation.

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Area of Science:

  • * Optics and Metrology
  • * Computer Vision and Machine Learning

Background:

  • * Traditional 3D phase measurement profilometry faces accuracy limitations due to device attitude variations.
  • * Existing methods require extensive mapping equations and memory, impacting efficiency.

Purpose of the Study:

  • * To develop an improved phase-to-height mapping method that accounts for device attitude.
  • * To enhance measurement accuracy and reduce computational complexity in 3D profilometry.

Main Methods:

  • * Obtaining unwrapped phase data of the target.
  • * Employing a generalized regression neural network (GRNN) to mitigate phase offsets caused by random device attitudes.
  • * Implementing an improved phase-to-height mapping by integrating the GRNN-processed phase data.

Main Results:

  • * The proposed method achieves high-quality phase-to-height mapping with fewer mapping equations and reduced memory usage.
  • * Demonstrated significant accuracy improvements: 44.30% over probabilistic neural network (PNN) and 39.58% over radial basis function neural network (RBFNN) methods.
  • * Successfully compensated for phase offsets due to device attitude variations.

Conclusions:

  • * The novel method effectively enhances the accuracy and efficiency of 3D phase measurement profilometry.
  • * GRNN-based attitude compensation offers a superior alternative to existing nonlinear mapping techniques.
  • * This approach is valuable for applications requiring precise 3D surface reconstruction.