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.

Weighted least squares phase unwrapping based on the wavelet transform

2007 ◽  
Author(s):  
Jiafeng Chen ◽  
Haiqin Chen ◽  
Zhengang Yang ◽  
Haixia Ren
Keyword(s):  
Wavelet Transform ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping

Least Squares Phase Unwrapping Algorithm Based on Topographic Factors

2011 ◽  
Vol 105-107 ◽  
pp. 1876-1879
Author(s):  
Wei Ke Liu ◽  
Gou Lin Liu ◽  
Xiao Qing Zhang
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping ◽  
Local Phase ◽  
Complex Signals ◽  
Topographic Factors ◽  
Interference Fringes ◽  
Phase Images ◽  
New Phase ◽  
Accuracy And Stability

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.

Weighted least-squares phase unwrapping algorithm based on a non-interfering image of an object

2017 ◽  
Vol 56 (15) ◽  
pp. 4543 ◽  
Author(s):  
Xian Wang ◽  
Suping Fang ◽  
Xindong Zhu
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping

scholarly journals Frequency Estimation of Irregularly Sampled Data Using a Sparsity Constrained Weighted Least-Squares Approach

2013 ◽  
Vol 3 (1) ◽  
pp. 368-372
Author(s):  
A. Zahedi ◽  
M. H. Kahaei
Keyword(s):  
Least Squares ◽  
Frequency Estimation ◽  
Weighted Least Squares ◽  
Sampled Data ◽  
Least Squares Fitting ◽  
Spectral Peaks ◽  
Data Record ◽  
Simulation Results ◽  
Irregularly Sampled Data ◽  
Least Squares Approach

In this paper, a new method for frequency estimation of irregularly sampled data is proposed. In comparison with the previous sparsity-based methods where the sparsity constraint is applied to a least-squares fitting problem, the proposed method is based on a sparsity constrained weighted least-squares problem. The resulting problem is solved in an iterative manner, allowing the usage of the solution obtained at each iteration to determine the weights of the least-squares fitting term at the next iteration. Such an appropriate weighting of the least-squares fitting term enhances the performance of the proposed method. Simulation results verify that the proposed method can detect the spectral peaks using a very short data record. Compared to the previous one, the proposed method is less probable to miss the actual spectral peaks and exhibit spurious peaks.

Divergence-Free Constrained Phase Unwrapping and Denoising for 4D Flow MRI Using Weighted Least-Squares

2021 ◽  
pp. 1-1
Author(s):  
Jiacheng Zhang ◽  
Sean M. Rothenberger ◽  
Melissa C. Brindise ◽  
Michael B. Scott ◽  
Haben Berhane ◽  
...  
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping ◽  
4D Flow Mri ◽  
4D Flow ◽  
Flow Mri ◽  
Divergence Free

scholarly journals Improved weighted least-squares phase unwrapping method for interferometric SAR processing

2019 ◽  
Vol 2019 (20) ◽  
pp. 6471-6474 ◽  
Author(s):  
Yu Hui ◽  
Wang Wenying ◽  
Zhuang Long ◽  
Lei Wanming ◽  
Nie Xin ◽  
...  
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping ◽  
Interferometric Sar ◽  
Sar Processing

Weighted least-squares phase unwrapping algorithm based on derivative variance correlation map

Optik ◽  
2007 ◽  
Vol 118 (2) ◽  
pp. 62-66 ◽  
Author(s):  
Yuangang Lu ◽  
Xiangzhao Wang ◽  
Xuping Zhang
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping
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