Weighted least-squares algorithm for phase unwrapping based on confidence level in frequency domain

2015 ◽  
Author(s):  
Shaohua Wang ◽  
Jie Yu ◽  
Cankun Yang ◽  
Shuai Jiao ◽  
Jun Fan ◽  
...  
Keyword(s):  
Least Squares ◽  
Frequency Domain ◽  
Confidence Level ◽  
Weighted Least Squares ◽  
Phase Unwrapping ◽  
Least Squares Algorithm

An iterative re-weighted least-squares algorithm for the design of active absorbing wavemaker controller

2016 ◽  
Vol 28 (2) ◽  
pp. 206-218 ◽  
Author(s):  
Hong-qi Yang ◽  
Mu-guo Li ◽  
Shu-xue Liu ◽  
Fang-mei Chen
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Least Squares Algorithm

scholarly journals A Robust Discontinuous Phase Unwrapping Based on Least-Squares Orientation Estimator

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2871
Author(s):  
Gaoxu Deng ◽  
Shiqian Wu ◽  
Shiyang Zhou ◽  
Bin Chen ◽  
Yucheng Liao
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Bilateral Filter ◽  
Phase Unwrapping ◽  
Noise Variance ◽  
Post Processing ◽  
Quadratic Error ◽  
Error Norm ◽  
Discontinuous Phase ◽  
Simulation Results

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.

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