Geometric dimension assisted absolute phase recovery in 3D shape measurement with digital fringe projection

Lv, Shenzhen; Zhang, Yuyuan; Jing, Lei; Wang, Jian; Liu, Ying; Sun, Quan

doi:10.1088/1361-6501/abd64f

Cited by 1 publication

(1 citation statement)

References 26 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional phase unwrapping methods can be broadly categorized into two groups: spatial phase unwrapping and temporal phase unwrapping method [16][17][18]. In practical applications, traditional methods still encounter several challenges, including inaccuracies caused by fringe pattern wrapping with noise and variable density phase images.…”

Section: Introductionmentioning

confidence: 99%

Depth_ASPP_UNet++: a novel 2D ESPI phase unwrapping based on convolutional neural network

Wang,

Liang,

Xue

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Phase unwrapping is a key step in optical metrology and physical optics to obtain accurate phase distributions. In practice, phase images obtained from electronic speckle pattern interferometry (ESPI) exhibit diverse and complex morphology, with significant shape variations and non-uniform densities among different individuals. This takes challenges for accurately extracting phase information and unwrapping the phase. With the progress of deep learning technology in optical image processing, real-time performance and accuracy have become concerned issues. In this paper, an ESPI phase unwrapping method based on convolutional neural network (CNN) UNet++ is proposed. The proposed network combines the depthwise separable convolution (DSC), atrous spatial pyramid pooling (ASPP), defined as Depth_ ASPP_ UNet++. In this model, the use of DSC improves network computational efficiency and provides better feature representation capability. In addition, ASPP is introduced to pay more attention to the phase information of the phase image, and then obtain better phase unwrapping results. The experimental results show that our proposed method can obtain excellent results, especially with various of variable density, different noise levels, and different speckle sizes.

show abstract

Section: Introductionmentioning

confidence: 99%