Weighted least squares phase unwrapping based on the wavelet transform

Author(s):  
Jiafeng Chen ◽  
Haiqin Chen ◽  
Zhengang Yang ◽  
Haixia Ren
Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2871
Author(s):  
Gaoxu Deng ◽  
Shiqian Wu ◽  
Shiyang Zhou ◽  
Bin Chen ◽  
Yucheng Liao

Weighted least-squares (WLS) phase unwrapping is widely used in optical engineering. However, this technique still has issues in coping with discontinuity as well as noise. In this paper, a new WLS phase unwrapping algorithm based on the least-squares orientation estimator (LSOE) is proposed to improve phase unwrapping robustness. Specifically, the proposed LSOE employs a quadratic error norm to constrain the distance between gradients and orientation vectors. The estimated orientation is then used to indicate the wrapped phase quality, which is in terms of a weight mask. The weight mask is calculated by post-processing, including a bilateral filter, STDS, and numerical relabeling. Simulation results show that the proposed method can work in a scenario in which the noise variance is 1.5. Comparisons with the four WLS phase unwrapping methods indicate that the proposed method provides the best accuracy in terms of segmentation mean error under the noisy patterns.


2011 ◽  
Vol 105-107 ◽  
pp. 1876-1879
Author(s):  
Wei Ke Liu ◽  
Gou Lin Liu ◽  
Xiao Qing Zhang

The phase of complex signals is wrapped since it can only be measured modulo-2; unwrapping searches for the 2-combinations that minimize the discontinuity of the unwrapped phase, as only the unwrapped phase can be analyzed and interpreted by further processing. Weighted least squares phase unwrapping algorithm could avoid errors transmission in the whole phase images, but it could not avoid defect and overlay of interference fringes caused by topographic factors. Therefore, a new phase unwrapping and weights choosing method based on local phase frequency estimate of topographic factors was presented. Experiments show it is an efficient phase unwrapping method which well overcome the defect of under-estimate slopes by least squares algorithm, and has higher accuracy and stability than other methods.


2017 ◽  
Vol 56 (15) ◽  
pp. 4543 ◽  
Author(s):  
Xian Wang ◽  
Suping Fang ◽  
Xindong Zhu

Author(s):  
Jiacheng Zhang ◽  
Sean M. Rothenberger ◽  
Melissa C. Brindise ◽  
Michael B. Scott ◽  
Haben Berhane ◽  
...  

2019 ◽  
Vol 2019 (20) ◽  
pp. 6471-6474 ◽  
Author(s):  
Yu Hui ◽  
Wang Wenying ◽  
Zhuang Long ◽  
Lei Wanming ◽  
Nie Xin ◽  
...  

Optik ◽  
2007 ◽  
Vol 118 (2) ◽  
pp. 62-66 ◽  
Author(s):  
Yuangang Lu ◽  
Xiangzhao Wang ◽  
Xuping Zhang

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