An Improved Quality-Guided Phase-Unwrapping Algorithm Based on Priority Queue

Zhong, Heping; Tang, Jinsong; Zhang, Sen; Chen, Ming

doi:10.1109/lgrs.2010.2076362

Cited by 79 publications

(36 citation statements)

References 7 publications

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“…Firstly, a quantized quality-guided phase unwrapping algorithm [4] is used to obtain the initial unwrapped result, then wrapped phase image are partitioned into high and low quality areas. In initial unwrapped result, the phase gradients of high quality areas are reliable, while in low quality areas which is unreliable due to the accumulative unwrapped phase errors.…”

Section: Combined Phase Unwrapping Algorithm a Basic Strategy Fomentioning

confidence: 99%

“…In the experiment, the quality map is defined by the phase derivative variance map in the 3×3 windows. The fast qualityguided phase unwrapping algorithm proposed in [4] is adopted and the quality level is defined as 1000. Fig.2(a) is an interferogram extracted of a TerraSAR image, which consists of 2048 × 2048 pixels.…”

Section: Performance Analysismentioning

confidence: 99%

“…In generation of high reliable phase quality map,Ghiglia and Pritt [1] have defined several quality maps which are useful in phase unwrapping, and there are still some other new phase quality maps have been proposed [2] [3]. In fast implementation of quality-guided process, a quantized quality-guided phase unwrapping algorithm has been proposed in [4], which makes the efficiency of the qualityguided phase unwrapping algorithm reach that of the branch cut method. The error accumulation in lower quality areas is the inherent drawback of quality-guided phase unwrapping algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A combined phase unwrapping algorithm for InSAR interferogram in shared memory environment

Zhong

Tian

Huang

et al. 2015

2015 8th International Congress on Image and Signal Processing (CISP)

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Phase unwrapping is a key step during the signal processing of interferometric synthetic aperture radar (InSAR) data. The precision and efficiency are two key problems of phase unwrapping. The path-following phase unwrapping algorithm usually has high computational efficiency, but low quality areas are prone to have accumulated errors. The global optimization phase unwrapping algorithm can effectively suppress the error propagation by introducing weights, but has low efficiency. This paper presents a combined phase unwrapping algorithm based on quality guided phase unwrapping and minimum discontinuity phase unwrapping algorithm, which improves the precision of the unwrapped result and greatly enhances the efficiency of unwrapping. First of all, according to phase quality map the wrapped phase image is divided into high and low quality areas, and the quantized quality-guided phase unwrapping algorithm is used to obtain the initial unwrapped results. Then low quality areas are grouped to balance the task distribution of low quality area's optimization. Finally, in shared memory parallel computation environment, grouped low quality areas are optimized by the minimum discontinuity phase unwrapping algorithm simultaneously using OpenMP parallel programming model. The tests performed on real InSAR interferogram show that the proposed method is effective.

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Section: Combined Phase Unwrapping Algorithm a Basic Strategy Fomentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A combined phase unwrapping algorithm for InSAR interferogram in shared memory environment

Zhong

Tian

Huang

et al. 2015

2015 8th International Congress on Image and Signal Processing (CISP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The problem of phase unwrapping has been extensively studied. It is encountered in, e.g., synthetic aperture radar (SAR) [14,15], magnetic resonance imaging [16], acoustic imaging, projection and diffraction tomography, and adaptive optics [17]. In many applications, the objective is to derive useful information from analysis of the unwrapped phase.…”

Section: Introductionmentioning

confidence: 99%

Frequency guided phase unwrapping for improved AM-FM reconstruction

Campbell

Havlicek

2014

2014 Southwest Symposium on Image Analysis and Interpretation

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We introduce a new phase unwrapping algorithm that makes it possible to obtain high fidelity image reconstructions from computed AM-FM models without the need for storing multiple boundary conditions such as phase samples from the original image in order to reconstruct the phase from the estimated FM field. This is important to the development of general modulation domain filters because the phase initial conditions are unknown after a filtering operation that modifies the FM functions, making reconstruction of the filtered image hard. In the new approach, frequency information from an initial least squares estimate of the unwrapped phase is used to guide selection of refined phase values that are congruent with the principal phase of the image. The selection process applies a queue-based region growing strategy to compute the final unwrapped phase solution with sparse branch cuts that tend to be placed only in areas with low visual impact. This final solution for the unwrapped phase leads to new solutions for the frequency modulations of the image that are in good agreement with visual perception and provide high quality reconstruction of the original image.

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“…PUMA is a generic form for the optimization based phase unwrapping method to let the user to select the potential function. The MF PU algorithm [16] and least squared methods use fixed potential function, which cannot be vary from experiments to experiments There are also a set of phase unwrapping algorithms based on the quality map to protect the discontinuous [17]- [19], but the quality map or residue mask should be acquired first.…”

mentioning

confidence: 99%

PUMA-SPA: A Phase Unwrapping Method Based on PUMA and Second-Order Polynomial Approximation

Hao

2014

IEEE Geosci. Remote Sensing Lett.

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This letter focuses on the phase unwrapping algorithm. A state-of-the-art phase unwrapping method called PUMA which is based on the max-flow/min-cut was proposed recently. The proposed method in this letter postprocesses the results of PUMA to improve the unwrapping results. A pointwise local second-order polynomial approximation method is considered to suppress the noise. We estimate the parameters of the polynomial by solving the overdetermined equations and get the solution with the Least Squares Error Fitting. The proposed algorithm synthesizes the unwrapping with the denoising method and is abbreviated as PUMA-SPA. In the denoising step, adaptive local window sizes are selected to compromise the fitting error and the suppression of noise. Experiments show that the proposed method can achieve better results than the method Congruence Operation and Least Squares Fitting (CO-LSF) proposed recently.Index Terms-Adaptive window size selection, local polynomial approximation (LPA), phase estimation, phase unwrapping.

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An Improved Quality-Guided Phase-Unwrapping Algorithm Based on Priority Queue

Cited by 79 publications

References 7 publications

A combined phase unwrapping algorithm for InSAR interferogram in shared memory environment

A combined phase unwrapping algorithm for InSAR interferogram in shared memory environment

Frequency guided phase unwrapping for improved AM-FM reconstruction

PUMA-SPA: A Phase Unwrapping Method Based on PUMA and Second-Order Polynomial Approximation

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