&lt;title&gt;Comparison of wavelet-based and statistical speckle filters&lt;/title&gt;

Hervet, Éric; Fjørtoft, R.; Marthon, Philippe; Lopès, A.

doi:10.1117/12.331361

Cited by 40 publications

(11 citation statements)

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“…Filtering in the transform domain has been extensively used during the last twenty years, such as wavelet transform [9,10,11,12], principal component analysis [13,14] and sparse representation [15,16,17]. Wavelet shrinkage can be readily applied to SAR despeckling after a homomorphic transformation.…”

Section: Introductionmentioning

confidence: 99%

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A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation

Fang

2018

Sensors

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In this paper, we propose a boosting synthetic aperture radar (SAR) image despeckling method based on non-local weighted group low-rank representation (WGLRR). The spatial structure information of SAR images leads to the similarity of the patches. Furthermore, the data matrix grouped by the similar patches within the noise-free SAR image is often low-rank. Based on this, we use low-rank representation (LRR) to recover the noise-free group data matrix. To maintain the fidelity of the recovered image, we integrate the corrupted probability of each pixel into the group LRR model as a weight to constrain the fidelity of recovered noise-free patches. Each single patch might belong to several groups, so different estimations of each patch are aggregated with a weighted averaging procedure. The residual image contains signal leftovers due to the imperfect denoising, so we strengthen the signal by leveraging on the availability of the denoised image to suppress noise further. Experimental results on simulated and actual SAR images show the superior performance of the proposed method in terms of objective indicators and of perceived image quality.

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Section: Introductionmentioning

confidence: 99%

“…Each wavelet subband is associated to a speckle contribution that may be exactly measured. Classical hard-thresholding and soft-thresholding methods are applied in [9,10], respectively. Some scholars [18,19,20] have performed a statistical Bayesian estimation to optimize the shrinkage parameter.…”

Section: Introductionmentioning

confidence: 99%

A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation

Fang

2018

Sensors

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“…In addition to the above, we also implemented a filter based on adaptable masks (Oddy [1]) and wavelet based Soft Thresholding [3], to show the implementability of these classes of methods in our framework.…”

Section: Figmentioning

confidence: 99%

“…Common despeckling methods include convolution filters (Mean, Gauss), rank operators (Median), methods based on local statistics (Lee, Kuan, Frost, Gamma MAP), filters with adaptable masks (Oddy), and wavelet based methods (Soft Thresholding). Reviews and comparisons of these methods are available in [1], [2], [3].…”

Section: Introductionmentioning

confidence: 99%

GPu-based framework for interactive visualization of SAR data

Lambers

Kolb

Nies

et al. 2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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Abstract-Synthetic Aperture Radar data presents specific problems for interactive visualization. The high amount of multiplicative speckle noise has to be reduced. The high dynamic range of the amplitude data must be mapped to the lower dynamic range of display devices in a way that makes image features appropriately visible. In addition to interactive navigation in the data, it is desirable to allow interactive selection of despeckling and dynamic range reduction methods and adjustment of their parameters.Graphics processing units (GPUs) can be seen as ubiquitous parallel coprocessors with extreme computational power. In this paper, we propose a GPU-based framework for interactive visualization of SAR data. Data management techniques are used to make full use of the GPU. We reworked well-known despeckling and dynamic range reduction techniques for the GPU programming model and implemented them in our framework. Both navigation in large data sets and adjustment of processing parameters are fully interactive.

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“…Gagon and Jouan [3] have used elliptic soft thresholding and shown that the waveletbased approach is better than the standard spatial domain filters. Nevertheless, log-transformation is a nonlinear operation, and thus provides a denoised image with a biased mean intensity [6].…”

Section: Introductionmentioning

confidence: 99%

A New Wavelet-based Method-for Despeckling SAR Images

Bhuiyan

Ahmad

Swamy

The 3rd International IEEE-NEWCAS Conference, 2005.

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In this paper, a wavelet-based despeckling method is proposed for suppressing speckle noise in synthetic aperture radar (SAR) images. A new threshold is proposed to classify the wavelet coefficients into significant and insignificant ones. Local statistic in the wavelet domain is used to further classify the significant coefficients into the edge and non-edge coefficients. The edge coefficients remain unaltered, whereas the non-edge and the insignificant ones are reduced in magnitude. Experiments are carried out on a noise-free image corrupted with simulated speckle noise, and a real SAR image. The results show that the proposed method provides a performance better than that of other methods in terms of the peak signal-to-noise ratio and ability to suppress speckle in the homogeneous areas. In addition, it introduces a bias that is much smaller than that of the other methods as well as preserves edges quite well.

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<title>Comparison of wavelet-based and statistical speckle filters</title>

Cited by 40 publications

References 0 publications

A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation

A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation

GPu-based framework for interactive visualization of SAR data

A New Wavelet-based Method-for Despeckling SAR Images

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