A robust phase unwrapping algorithm based on reliability mask and weighted minimum least-squares method

Yan, Liping; Zhang, Haiyan; Zhang, Rui; Xie, Xin; Chen, Benyong

doi:10.1016/j.optlaseng.2018.08.024

Cited by 28 publications

(11 citation statements)

References 16 publications

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“…According to the Equations (12) and (13) and the least-squares method [59], the strain diagrams could be calculated. Based on the selected sample (#1~#4), the phase diagrams and strain diagrams which caused by end-face load-driven were obtained respectively.…”

Section: Methodsmentioning

confidence: 99%

A Strain Distribution Sensing System for Bone-Implant Interfaces Based on Digital Speckle Pattern Interferometry

Zhong

Yang

et al. 2019

Sensors

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This paper aims to provide an effective measurement method for the distribution of deformations and strains focusing on the response to external loading of bone-implant interfaces. To achieve this target, a novel speckle interference imaging method is proposed by introducing phosphate buffer saline medium, in which the samples were completely placed into a phosphate buffer saline solution medium to stable the water molecules. The stability of interferometry imaging is analyzed by using the concepts of co-occurrence matrix and moment of inertia. A series of experiments to measure load-driven deformation and strain in the bone-implant interface was carried out, and the experiments results were analyzed and discussed. It shows that the proposed method is feasible and effective for the no-contact strain measurements of biomaterials in a physiological condition. The proposed strain distribution sensing system will contribute to evaluating computational simulations and improving selection of implant designs and materials.

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Section: Methodsmentioning

confidence: 99%

A Strain Distribution Sensing System for Bone-Implant Interfaces Based on Digital Speckle Pattern Interferometry

Zhong

Yang

et al. 2019

Sensors

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“…In this section, we report the results of a quantitative evaluation of the weight mask's segmentation performance of the proposed method and compare with the state-of-the-art methods, such as Li's method [22] (orientation coherence, OC), Lu's method [13] (derivative variance correlation map, DVCM), Cui's method [15] (phase Laplace derivative variance, PLDV), and Yan's method [21] (reliability mask, RM). All algorithms were implemented in MATLAB R2018b programming on a laptop equipped with an Intel (R) Core (TM) i5-8250U processor with a clock frequency of 1.6 GHz and 8 GB of RAM.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…Furthermore, a complete solution based on [19] was described in [20] to deal with discontinuous phase unwrapping problems. To solve the weak global noise problem, Yan et al [21] proposed a WLS phase unwrapping algorithm based on a reliability mask. For different patterns, several trials were carried out to determine the appropriate threshold.…”

Section: Introductionmentioning

confidence: 99%

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

Deng

Zhou

et al. 2021

Electronics

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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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“…Since phase unwrapping is an ill-posed problem, the phase continuity assumption is usually considered in the process of phase unwrapping: the absolute values of the gradients in the two directions of the unwrapped phase are less than π [6]. Under this assumption, many kinds of phase unwrapping methods have been presented in recent decades, and they can be divided into two categories: path following [5,7,8] and optimization-based methods [9][10][11][12][13][14][15]. A path following method selects the integration path for integrating the estimated phase gradient through the residue distribution or the phase quality map, so as to avoid the local error from being propagated globally.…”

Section: Introductionmentioning

confidence: 99%

“…The LS phase unwrapping method is widely used in practical applications and converges quickly [9,26,27]; therefore, we considered combining it and deep learning to improve the unwrapping accuracy while retaining the advantages of the LS method. In the traditional LS method, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step.…”

Section: Introductionmentioning

confidence: 99%

A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

Zhou

et al. 2021

Remote Sensing

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Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise.

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A robust phase unwrapping algorithm based on reliability mask and weighted minimum least-squares method

Cited by 28 publications

References 16 publications

A Strain Distribution Sensing System for Bone-Implant Interfaces Based on Digital Speckle Pattern Interferometry

A Strain Distribution Sensing System for Bone-Implant Interfaces Based on Digital Speckle Pattern Interferometry

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

A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

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