High-Accuracy 3D Contour Measurement by Using the Quaternion Wavelet Transform Image Denoising Technique

Abstract

In this paper, we propose an image denoising algorithm based on the quaternion wavelet transform (QWT) to address sinusoidal fringe images under strong noise in structured light 3D profilometry. The analysis of a quaternion wavelet shows that the amplitude image of the quaternion wavelet is easily affected by noise. However, the three phase images, which mainly reflect edge and texture information, are randomly and disorderly distributed with respect to noise. The QWT denoising algorithm is suitable for processing sinusoidal fringe images of complex structures in a high-accuracy 3D measurement system. Sinusoidal fringe images are collected and denoised by using the QWT algorithm and classical Gaussian smoothing (GS) denoising algorithm, and GS is used as a reference for the QWT algorithm. The results indicate that the standard deviation is reduced from 0.1448 for raw sinusoidal fringe images to 0.0192, and the signal-to-noise ratio is improved from 4.6213 dB to 13.3463 dB by using the QWT algorithm. The two algorithms have the same denoising effect for a surface with less information. For a surface with rich information, the details of the 3D contour are lost because of the image “blurring” caused by using the GS algorithm, while all edge details of the 3D contour are reconstructed by using the QWT denoising algorithm because of its characteristic of information and noise being separated from the source. For the measured face mask, the error is less than ±0.02 mm. In addition, it takes less than 20 s to run the QWT algorithm to process eight sinusoidal fringe images, which meets the requirements of high-precision measurements.