An Interferometric Phase Noise Reduction Method Based on Modified Denoising Convolutional Neural Network

Li, Shuo; Xu, Huaping; Gao, Shuai; Liu, Wei; Li, Chunsheng; Liu, Aifang

doi:10.1109/jstars.2020.3017808

Cited by 14 publications

(11 citation statements)

References 40 publications

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“…In the first step, we evaluate a single value of noise standard deviation using both spatial as well as transform domain information of image assuming that the noise follows Gaussian distribution across the image. This step can also be independently used for Aja-Fern andez et al (Rician) 16 Liu et al 17 Li et al 7 Maximov et al 32 estimation of spatially invariant noise. In the second step, the noise correction factor is evaluated using the relationship between Gaussian and RN.…”

Section: Discussionmentioning

confidence: 99%

“…Spatially invariant Rician noise (RN) was widely accepted in literature as an appropriate model of noise in MRI. [6][7][8][9][10][11][12][13][14] The main assumption was a single coil MR acquisition; hence, in all the cases, the noise in the MRI is considered to be spatially homogeneous and hence a single value of noise standard deviation was sufficient to characterize the entire dataset. In the present MRI acquisition scenario, such presumptions certainly fail due to the complexity of fast imaging with multicoil for parallel imaging and reconstruction and local field inhomogeneities.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Nonhomogeneous Noise in 2D Magnetic Resonance Imaging

Shukla

Khandekar

Khaparde

2021

Int J Imaging Syst Tech

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Magnetic resonance images are often influenced by noise. Reliable estimation of noise characteristics is important for image post-processing. The benchmark algorithms often assume a uniform noise model across the image; that is, the noise features are spatially invariant. A few noise estimators are found to cope with nonhomogeneous noise; however, they either require multiple acquisitions or other additional information. In this research work, we develop a method that can accurately estimate the nonhomogeneous parameters of noise from just a single magnitude image. The proposed algorithm is a two-step approach. In the first step, we evaluate the standard deviation of noise assuming that it follows Gaussian distribution. This step utilizes a hybrid of spatial domain edge information suppression and transforms domain coefficient statistics to estimate a single value of standard deviation of noise in magnitude MR images. In the second step, the evaluated noise is corrected using the analytical expression that establishes the relationship between Gaussian noise and Rician noise. Results on synthetic and clinical data evidence the better performance of the proposed algorithm when compared to the benchmark methods.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Nonhomogeneous Noise in 2D Magnetic Resonance Imaging

Shukla

Khandekar

Khaparde

2021

Int J Imaging Syst Tech

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“…To quantitatively compare the filtering results of different methods, phase root mean square error (RMSE), edge-preserving index (EPI), the number of residual points and filtering time, are introduced to evaluate the filtered phases. Their definition can be found in [32].…”

Section: A Simulated Datamentioning

confidence: 99%

“…Thus, the performance of the filtering results is evaluated with the number of residual points, residual phase deviation (RPSD) and filtering time. The RPSD is calculated after removing the local fringes, whose definition and calculation formula can be found in [32].…”

Section: B Real Data 1) Ers Sar Datamentioning

confidence: 99%

A Nonlocal Noise Reduction Method Based on Fringe Frequency Compensation for SAR Interferogram

Xu¹,

Li²,

Li³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Phase noise reduction is one of the key steps for InSAR data processing. In this paper, a novel phase filtering method is proposed. The main innovation and contribution of this research is to, (1) incorporate local fringe frequency (LFF) compensation technique into the non-local phase filtering method to include more independent and identically distributed (i.i.d.) samples for filtering; (2) modify the non-local phase filter from three aspects: 1) executing non-local filtering in the complex domain of the residual phase to avoid gray jumps in phase, 2) adaptively calculating the smoothing parameter based on the LFF and the coherence coefficient, and 3) using the integral image in similarity calculation to improve the efficiency; (3) perform Goldstein filter in high coherence areas to reduce the computation expense. Experiments based on both simulated and real data have shown that the proposed method has achieved a better performance in terms of both noise reduction and edge preservation than some existing phase filtering methods.

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“…2018 proposed an InSAR‐BM3D filtering method based on block matching and 3D filtering (BM3D) algorithm, which used the decorrelating transform to separate the real and imaginary parts and look for similar structures instead of looking for similar blocks directly to the original phase. With the development of signal processing methods, sparse signal processing methods (Çetin et al., 2014; Potter et al., 2010) and filtering methods based on deep learning (Zhang et al., 2016; Zhu et al., 2017) and convolutional neural networkhave (CNN) also been proposed (Li et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

A Fast Non‐Local Means Filtering Method for Interferometric Phase Based on Wavelet Packet Transform

2021

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Phase unwrapping is very important to acquire surface deformation using InSAR, unwrapping the interferometric phase directly is not appropriate because the phase is affected by various noises. Therefore, it is essential to apply a suitable filter to the interferometric phase before phase unwrapping. Although there are many filtering methods for noise, these methods cannot be used directly due to the particularity of interferometric phase noise and therefore it is important to do the corresponding domain transformation. Here, a fast non‐local means filtering method based on wavelet packet transform is proposed. Originally, wavelet packet transform is performed on the real part and the imaginary part respectively, which avoids the influence of phase jumps on the subsequent filtering. Furthermore, the fast non‐local means filtering can be applied to smooth the acquired wavelet packet coefficients. Eventually, inverse transform of the wavelet packet is used to reconstruct the phase after filtering. Compared with other filtering algorithms in simulated and actual Sentinel‐1 data, the superiority of this algorithm is proved.

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An Interferometric Phase Noise Reduction Method Based on Modified Denoising Convolutional Neural Network

Cited by 14 publications

References 40 publications

Estimation of Nonhomogeneous Noise in 2D Magnetic Resonance Imaging

Estimation of Nonhomogeneous Noise in 2D Magnetic Resonance Imaging

A Nonlocal Noise Reduction Method Based on Fringe Frequency Compensation for SAR Interferogram

A Fast Non‐Local Means Filtering Method for Interferometric Phase Based on Wavelet Packet Transform

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