An Enhanced High-Order Variational Model Based on Speckle Noise Removal With $G^0$  Distribution

Mu, Yunping; Huang, Baoxiang; Pan, Zhenkuan; Yang, Huan; Hou, Guiting; Duan, Jinming

doi:10.1109/access.2019.2931581

Cited by 9 publications

(2 citation statements)

References 57 publications

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“…Synthetic Aperture Radar (SAR) technology has become essential for environmental monitoring and disaster management. It provides valuable images under various conditions, including day or night and weather situations [1,2]. However, the effective use of SAR data depends on a thorough understanding of its statistical properties because it is corrupted by speckle.…”

Section: Introductionmentioning

confidence: 99%

Identifying Heterogeneity in SAR Data with New Test Statistics

Frery,

Alpala,

Nascimento

2024

Remote Sensing

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This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully developed speckle, i.e., when there are infinitely many independent backscatterers per resolution cell, and none dominates the return. Such areas are often called “homogeneous” or “textureless” regions. The GI0 distribution is also a widely accepted law for heterogeneous and extremely heterogeneous regions, i.e., areas where the fully developed speckle hypotheses do not hold. We propose three test statistics to distinguish between homogeneous and inhomogeneous regions, i.e., between gamma and GI0 distributed data, both with a known number of looks. The first test statistic uses a bootstrapped non-parametric estimator of Shannon entropy, providing a robust assessment in uncertain distributional assumptions. The second test uses the classical coefficient of variation (CV). The third test uses an alternative form of estimating the CV based on the ratio of the mean absolute deviation from the median to the median. We apply our test statistic to create maps of p-values for the homogeneity hypothesis. Finally, we show that our proposal, the entropy-based test, outperforms existing methods, such as the classical CV and its alternative variant, in identifying heterogeneity when applied to both simulated and actual data.

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Section: Introductionmentioning

confidence: 99%

Identifying Heterogeneity in SAR Data with New Test Statistics

Frery,

Alpala,

Nascimento

2024

Remote Sensing

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“…Additionally, a box constraint together with the K‐means clustering method was used to attenuate the staircase artifact and preserve the edge. In Reference 25, a variational model was established by adding a high‐order total curvature term to the first‐order TV. The results demonstrated that the staircase artifact was avoided and the feature of the image was preserved.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of conductivity distribution with a compound variational strategy in electrical impedance tomography

Shi

Tian

Wang

et al. 2021

Int J Imaging Syst Tech

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As a potential imaging technique, electrical impedance tomography (EIT) is advantageous for reconstructing conductivity distribution. However, due to insufficient measurement, visualization is inevitably an ill-posed inverse problem. Moreover, reconstruction quality is affected by noise. To address these challenges, a novel variational model with an L p -norm as fidelity and a hybrid total variation as penalty (L p -HTV) is proposed for conductivity distribution reconstruction. Iterative reweighted L 1 algorithm transforms the L p -norm to an L 1 -norm and alternating direction method of multipliers is then utilized to solve objective function. Meanwhile, region of interest is defined to enhance robustness to noise and reduce staircase artifact. The performance of the proposed compound method is validated by several cases. Phantom experiments are also conducted. Compared with classic regularization methods, it is found that images reconstructed by the proposed method show large improvement. The results demonstrate that the proposed strategy is more competitive in visualizing conductivity distribution.

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