2023
DOI: 10.3390/s23020680
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High-Accuracy Three-Dimensional Deformation Measurement System Based on Fringe Projection and Speckle Correlation

Abstract: Fringe projection profilometry (FPP) and digital image correlation (DIC) are widely applied in three-dimensional (3D) measurements. The combination of DIC and FPP can effectively overcome their respective shortcomings. However, the speckle on the surface of an object seriously affects the quality and modulation of fringe images captured by cameras, which will lead to non-negligible errors in the measurement results. In this paper, we propose a fringe image extraction method based on deep learning technology, w… Show more

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Cited by 6 publications
(2 citation statements)
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“…In this case, the SPPWM pattern is significantly affected by higher harmonics and speckle patterns. In the case where traditional algorithms cannot simultaneously eliminate the interference of high-order harmonics and speckle patterns, based on the potential of deep learning in eliminating high-order harmonics [45] and noise [32,46], we proposed a sinusoidal pattern reconstruction network (SPRNet) that can simultaneously eliminate high-order harmonics and speckle patterns in SPPWM patterns to obtain high-quality sinusoidal patterns. In addition, considering the distribution characteristics of speckle patterns in SPPWM pattern, we proposed a multi-temporal spatial correlation matching algorithm (MTSCMA), which achieves a more reliable coarse disparity map calculation.…”
Section: Introductionmentioning
confidence: 99%
“…In this case, the SPPWM pattern is significantly affected by higher harmonics and speckle patterns. In the case where traditional algorithms cannot simultaneously eliminate the interference of high-order harmonics and speckle patterns, based on the potential of deep learning in eliminating high-order harmonics [45] and noise [32,46], we proposed a sinusoidal pattern reconstruction network (SPRNet) that can simultaneously eliminate high-order harmonics and speckle patterns in SPPWM patterns to obtain high-quality sinusoidal patterns. In addition, considering the distribution characteristics of speckle patterns in SPPWM pattern, we proposed a multi-temporal spatial correlation matching algorithm (MTSCMA), which achieves a more reliable coarse disparity map calculation.…”
Section: Introductionmentioning
confidence: 99%
“…Unfortunately, the practicality of using median filtering to remove speckles depends on the size and color depth of the speckles, which makes it ineffective in certain situations. Zhang [ 34 ] and others proposed a fringe image extraction method based on deep learning technology that transformed speckle-embedded fringe images into speckle-free fringe images. However, deep learning methods require training a large number of samples and may not be able to cope with complex surface conditions of objects.…”
Section: Introductionmentioning
confidence: 99%