Speckle reduction by adaptive window anisotropic diffusion

Liu, Guojin; Zeng, Xiaoping; Tian, Fengchun; Li, Zhengzhou; Kadri, Chaibou

doi:10.1016/j.sigpro.2009.04.042

Cited by 45 publications

(12 citation statements)

References 29 publications

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“…Step 1: Calculate the value of the edge detection operator f of the image pixel-by-pixel according to Equation (21), Equation (22), and Equation (23); Step 2: Calculate the anisotropic diffusion threshold T according to Equation (24);…”

Section: Methods and Processesmentioning

confidence: 99%

“…It should be noted that the estimation of the noise variation coefficient f 2 (u) has a critical influence on the result of the algorithm. Liu et al [21] proposed the AWAD algorithm to improve speckle reduction by adaptively changing the size of the filter window on the basis of SRAD. The height and width of the filter window can be obtained using Equations (12) and (13):…”

Section: Improved Algorithm Based On Sradmentioning

confidence: 99%

See 1 more Smart Citation

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: Methods and Processesmentioning

confidence: 99%

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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“…Popular methods include the Lee method [1], the multiscale shrinkage methods [2], various anisotropic diffusion based methods [3,4], and variational methods [5][6][7][8][9][10][11][12][13][14][15]. The first TV based multiplicative noise removal model was presented by Rudin et al [7], which used a constrained optimization approach with two Lagrange multipliers.…”

Section: Introductionmentioning

confidence: 99%

A variational model based on split Bregman method for multiplicative noise removal

et al. 2015

AEU - International Journal of Electronics and Communications

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“…Ramp-Preserving PeronaMalik model (RPPM) [22] made use of a diffusion coefficient which avoids the stair casing effect and preserves edges. The Adaptive Window Anisotropic Diffusion [23] utilizes a variable size and orientation window to achieve speckle suppression. Though the techniques discussed above performed satisfactory well for lower speckle contents; but their performance gets limited at high speckle noise variances.…”

Section: Introductionmentioning

confidence: 99%

Despeckling of ultrasound images using non-linear conductance function

Bhateja

Singh

Srivastava

et al. 2014

2014 International Conference on Signal Processing and Integrated Networks (SPIN)

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This paper presents a novel anisotropic diffusion (AD) filter for despeckling of ultrasound images using a non-linear conductance function. Proper choice of the conductance function while applying AD filters is extremely critical. This work proposes an improved conductance function that tends to suppress speckle with due preservation of diagnostic features. The diffusion coefficient of the AD filter thus decreases monotonically and acts as an edge seeking as well as preserving function. Peak Signal to Noise Ratio (PSNR), Coefficient of Correlation (CoC) and Speckle Suppression Index (SSI) are the quality metrics used for performance evaluation of the proposed despeckling filter.

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Speckle reduction by adaptive window anisotropic diffusion

Cited by 45 publications

References 29 publications

Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

A variational model based on split Bregman method for multiplicative noise removal

Despeckling of ultrasound images using non-linear conductance function

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