Gamma error correction algorithm for phase shift profilometry based on polar angle average

Cai, Bolin; Tong, Chenen; Wu, Qiujie; Chen, Xiangcheng

doi:10.1016/j.measurement.2023.113074

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…One of these techniques is to obtain 3D information of objects by means of 2D images using phase shift profilometry (PSP) on objects [1,2]. However, this technique produces noise in the images, known as Moire noise, due to the use of a fringe pattern projected on the object, which affects the accuracy and final reconstruction of the object to be reconstructed and the extraction of 3D information [3,4]. The reduction and/or elimination of periodic or quasi/periodic noise, known as Moire noise, began as soon as the first digital images could be obtained; however, it was not until it was analyzed as to how such noise was produced that research began on ways to attenuate or eliminate it from the images.…”

Section: Introductionmentioning

confidence: 99%

Quasi/Periodic Noise Reduction in Images Using Modified Multiresolution-Convolutional Neural Networks for 3D Object Reconstructions and Comparison with Other Convolutional Neural Network Models

Espinosa-Bernal,

Pedraza-Ortega,

Aceves-Fernandez

et al. 2024

Computers

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The modeling of real objects digitally is an area that has generated a high demand due to the need to obtain systems that are able to reproduce 3D objects from real objects. To this end, several techniques have been proposed to model objects in a computer, with the fringe profilometry technique being the one that has been most researched. However, this technique has the disadvantage of generating Moire noise that ends up affecting the accuracy of the final 3D reconstructed object. In order to try to obtain 3D objects as close as possible to the original object, different techniques have been developed to attenuate the quasi/periodic noise, namely the application of convolutional neural networks (CNNs), a method that has been recently applied for restoration and reduction and/or elimination of noise in images applied as a pre-processing in the generation of 3D objects. For this purpose, this work is carried out to attenuate the quasi/periodic noise in images acquired by the fringe profilometry technique, using a modified CNN-Multiresolution network. The results obtained are compared with the original CNN-Multiresolution network, the UNet network, and the FCN32s network and a quantitative comparison is made using the Image Mean Square Error E (IMMS), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Profile (MSE) metrics.

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Section: Introductionmentioning

confidence: 99%