Efficient phase unwrapping algorithm based on cubature information particle filter applied to unwrap noisy continuous phase maps

Xie, Xiaogao; Zeng, Qingning; Liao, Kefei; Liu, Qinghua

doi:10.1364/oe.27.009906

Cited by 16 publications

(7 citation statements)

References 48 publications

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“…The PSNR and the standard deviation of phase error that are caused by Gaussian noise (STD) are used as indicators for filtering evaluation. The proposed FOPDEs method is compared with the commonly used methods, such as Gaussian filtering [16,17,18,21], mean filtering [16,17,18], wavelet transform [17], TV [19,20], and OPDEs [24], as shown in Table 1. Table 1 reflects the results of adding uniform noise to phase-shifting images and adding different noise to different phase-shifting images.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…However, the edges of binary patterns are often difficult to precisely distinguish, which reduces the measurement accuracy. The filtering method [16,17,18,19,20,21,22,23,24] is to reduce noise by means of filtering algorithms. Image filtering is a process of restoring noise-free image from noise image, in which the difficulty is how to protect details while reducing noise.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, how to filter the Gaussian noise effectively is very important in accurately extracting the phase information. Traditional filtering methods [16,17,18,19,20], such as Gaussian filtering, median filtering, and wavelet transform, were proposed to reduce the noise. Villa [21] proposed a fringe pattern denoising method based on Gaussian convolution to improve the performance of low-frequency fringes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Xue

et al. 2019

Sensors

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Traditional filtering methods only focused on improving the peak signal-to-noise ratio of the single fringe pattern, which ignore the filtering effect on phase extraction. Fringe phase-shifting field based fuzzy quotient space-oriented partial differential equations filtering method is proposed to reduce the phase error caused by Gaussian noise while filtering. First, the phase error distribution that is caused by Gaussian noise is analyzed. Furthermore, by introducing the fringe phase-shifting field and the theory of fuzzy quotient space, the modified filtering direction can be adaptively obtained, which transforms the traditional single image filtering into multi-image filtering. Finally, the improved fourth-order oriented partial differential equations with fidelity item filtering method is established. Experiments demonstrated that the proposed method achieves a higher signal-to-noise ratio and lower phase error caused by noise, while also retaining more edge details.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Xue

et al. 2019

Sensors

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“…Of course, the coefficient can also be adjusted in the opposite direction, for example, an embedded watermark with a coefficient of −1 is selected. Then, all coefficients smaller than −1 are adjusted by an amplitude in the negative direction to generate a gap [ 13 ]. Ordinary wavelet transform cannot be used in reversible watermarking because it does not support complete reversibility.…”

Section: Wavelet Transform and Information Hiding Technology In Plaintext Domainmentioning

confidence: 99%

Reversible Information of Encrypted Image Based on Feature Difference Detection and Wavelet Transform

Ding

Yun-bo

Zhang

et al. 2021

Contrast Media & Molecular Imaging

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Aiming at the shortcomings of the existing lossless digital watermarking algorithm based on frequency domain in reversibility and embedding capacity, this study proposes a lossless digital image watermarking algorithm based on fractional wavelet transform, which is used for large-capacity reversible information hiding of images. First, the image is transformed by LeGall5/3 fractional wavelet, and then, the watermark is embedded in the high-frequency subband by the histogram shift method. In order to obtain maximum embedding capacity and reduce image distortion, the methods of selecting embedding parameters and stopping parameters are proposed, respectively. At the same time, in order to prevent overflow and reduce additional information, a new method of generating position map is proposed. The experimental results show that Lena is the result of multilayer embedding based on the algorithm in this study. In order to better observe the distortion phenomenon and enlarge the image, the Lena test image is the watermark image obtained after two and three layers of embedding, and its embedding capacity can be 2.7 bpp. It is proved that wavelet transform is suitable for encrypted images to implement covert communication.

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“…Equation ( 8 As a result, the spatial domain methods are more widely used in the actual applications and they can be divided into two categories: path dependent and path independent. The pathdependent methods are based on a path integration strategy to obtain the unwrapped phase map where a lot of strategies have been proposed, such as the mask cut algorithms [18][19][20][21][22][23][24][25], the quality-guided path following algorithms [26][27][28][29][30][31][32][33][34][35][36][37], and the Flynn's algorithms [38][39][40]. The path-independent methods seek to find an unwrapped phase whose spatial differences match the differences in the wrapped phase data as closely as possible, in which the phase unwrapping is completed by minimization of a global function.…”

Section: Comparative Study Of Phase Unwrapping Algorithms Based On So...mentioning

confidence: 99%

Comparative study of phase unwrapping algorithms based on solving the Poisson equation

Zhao

Zhang

et al. 2020

Meas. Sci. Technol.

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Phase unwrapping is a crucial process to obtain the absolute phase profile in many optical phase measurement techniques such as interferometry, holography, profilometry, etc. In this paper, we have studied several phase unwrapping algorithms based on solving the discrete Poisson equation. The differences among those algorithms lie in two aspects: one is the calculation of the input for the Poisson equation using the wrapped phase data and the other is the way to compute the output (unwrapped phase data) using the corresponding input. Firstly, the method to compute the input for the Poisson equation was investigated using the finite difference and fast Fourier transform (FFT) methods. Then different methods, based on FFT or discrete cosine transform, were employed to calculate the unwrapped phase, and their performances were compared in terms of accuracy and efficiency. To enhance the precision of those algorithms, an iteration strategy was introduced and its performance was investigated under different noise conditions. Finally, several pieces of real phase data was tested by using the direct and iterative methods. The detailed software package can be found online (www.mathworks.com/matlabcentral/ fileexchange/71810-phase-unwrapping-algorithms-by-solving-the-poisson-equation).

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Efficient phase unwrapping algorithm based on cubature information particle filter applied to unwrap noisy continuous phase maps

Cited by 16 publications

References 48 publications

Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Reversible Information of Encrypted Image Based on Feature Difference Detection and Wavelet Transform

Comparative study of phase unwrapping algorithms based on solving the Poisson equation

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