Dual-Tree Complex Wavelet Transform Based SAR Despeckling Using Interscale Dependence

Ranjani, J. Jennifer; Thiruvengadam, S. J.

doi:10.1109/tgrs.2010.2041241

Cited by 91 publications

(45 citation statements)

References 28 publications

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“…w = a g 2 + 1 (13) where h and w denote the height and width of the filter window, a is the scale parameter, and g 1 and g 2 denote the maximum and minimum direction derivatives of pixel. Additionally, the AWAD algorithm defines a new diffusion function:…”

Section: Improved Algorithm Based On Sradmentioning

confidence: 99%

Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

Feng

Zhang

et al. 2018

Remote Sensing

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Abstract:To better reduce image speckle noise while also maintaining edge information in synthetic aperture radar (SAR) images, we propose a novel anisotropic diffusion algorithm using weighted Euclidean distance (WEDAD). Presented here is a modified speckle reducing anisotropic diffusion (SRAD) method, which constructs a new edge detection operator using weighted Euclidean distances. The new edge detection operator can adaptively distinguish between homogenous and heterogeneous image regions, effectively generate anisotropic diffusion coefficients for each image pixel, and filter each pixel at different scales. Additionally, the effects of two different weighting methods (Gaussian weighting and non-linear weighting) of de-noising were analyzed. The effect of different adjustment coefficient settings on speckle suppression was also explored. A series of experiments were conducted using an added noise image, GF-3 SAR image, and YG-29 SAR image. The experimental results demonstrate that the proposed method can not only significantly suppress speckle, thus improving the visual effects, but also better preserve the edge information of images.

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Section: Improved Algorithm Based On Sradmentioning

confidence: 99%

Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

Feng

Zhang

et al. 2018

Remote Sensing

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“…The Cauchy PDF is used for modeling the co-efficient of log transformed reflectance [26] Depending on the nature of the wavelet coefficient, the thresholding types have been classified as universal, bayesion shrink and wavelet threshold. All thresholding techniques are finally compared with spatial domain filters and have been proved better than spatial filters.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…The idea of implementing Dual Tree Complex Wavelet Transform (DTCWT) based SAR despeckling involving inter scale dependencies between wavelet coefficients [26]was suggested. The DTCWT has been organized as one tree which provides real co-efficient and another one provides imaginary coefficients and so it provides both magnitude and phase information.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…The coefficients of DTCWT after log transformation at level j and orientation i is given by (11) This method makes use of Mellin transform to find dispersion parameter of Bivariate Cauchy PDF and using the filter, bivariate MAP which is very much advantageous than earlier methods of numerical integration, is employed/obtained. It has the limitation of increase in decomposition level and causes blurring of despeckled image [26].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

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Performance Analysis Survey of Various SAR Image Despeckling Techniques

Rajamani¹,

Krishnaveni²

2014

IJCA

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Over the past four decades, the Synthetic Aperture Radar (SAR) imagery has become a beneficial and important application over the optical satellite imagery because of its ability to operate in any weather conditions. However, these images are affected with granular noise termed as Speckle noise. This noise affects the overall quality of the image adversely and hence hinders the observation of vital and crucial information present in the image. Thus, it has become essential to remove this speckle noise using suitable techniques. This paper presents the various important techniques available till date for the removal of speckle noise from SAR images and each technique with its own advantages and limitations are described. It also presents qualitative and quantitative measures of various techniques.

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“…Those superresolution imaging (Celik and Tjahjadi, 2010) and despeckling of synthetic aperture radar data were proposed (Ranjani and Thiruvengadan, 2010). In image fusion, the DT-CWT fusion method with SW method (Ioannidou and Karathanassi, 2007) and a DT-CWT fusion method with an injection model have been proposed (Renza et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Fast and Efficient Satellite Imagery Fusion Using DT-CWT Proportional and Wavelet Zero-Padding

Kim¹,

Oh²,

Kim³

2015

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Among the various image fusion or pan-sharpening methods, those wavelet-based methods provide superior radiometric quality. However, the fusion processing is not only simple but also flexible, since many low-and high-frequency sub-bands are often produced in the wavelet domain. To address this issue, a novel DT-CWT (Dual-Tree Complex Wavelet Transform) proportional to the fusion method by a WZP (Wavelet Zero-Padding) is proposed. The proposed method produces a single high-frequency image in the spatial domain that is injected into the LRM (Low-Resolution Multispectral) image. Thus, a wavelet domain fusion can be simplified to spatial domain fusion. In addition, in the proposed DT-CWTP (DT-CWT Proportional) fusion method, it is unnecessary to decompose the LRM image by adopting WZP. The comparison indicates that the proposed fusion method is nearly five times faster than the DT-CWT with SW (Substitute-Wavelet) fusion method, meanwhile simultaneously maintaining the radiometric quality. The conducted experiments with WorldView-2 satellite images demonstrated promising results with the computation efficiency and fused image quality. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Dual-Tree Complex Wavelet Transform Based SAR Despeckling Using Interscale Dependence

Cited by 91 publications

References 28 publications

Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

Performance Analysis Survey of Various SAR Image Despeckling Techniques

Fast and Efficient Satellite Imagery Fusion Using DT-CWT Proportional and Wavelet Zero-Padding

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