Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

Feng, Guo; Zhang, Guo; Zhang, Qingjun; Zhao, Ruishan; Deng, Minghua; Kai, Xu

doi:10.3390/rs10050722

Cited by 21 publications

(11 citation statements)

References 27 publications

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“…The AD filtering method employs a gradient operator to identify the gradient changes in the image that are caused by noise and the edge effect. Nearest-neighbor weighted averaging removed small gradient changes caused by noise, while large gradient changes that are caused by edges are preserved [3]. This technique of the AD filter obtains satisfactory results with respect to smoothing additive noise in the image.…”

Section: Introductionmentioning

confidence: 92%

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Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Choi

Jeong

2019

Remote Sensing

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Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.

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Section: Introductionmentioning

confidence: 92%

“…[2]. Due to the advantages of SAR images and their various applications, research on the technology behind SAR images is being actively conducted around the world (image enhancement [3][4][5], image classification [6,7], image segmentation [8,9], etc. ).…”

Section: Introductionmentioning

confidence: 99%

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Choi

Jeong

2019

Remote Sensing

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“…TD filters often use the wavelet transform [18], curvelet transform [19], and shearlet transform [20] for speckle suppression. However, although TD filters suppress noise well, they have high computational complexity [21]. For speckle suppression, a good speckle filter should provide excellent speckle smoothing, edge preservation, and radiometric preservation [1,17,21].…”

Section: Introductionmentioning

confidence: 99%

“…However, although TD filters suppress noise well, they have high computational complexity [21]. For speckle suppression, a good speckle filter should provide excellent speckle smoothing, edge preservation, and radiometric preservation [1,17,21]. AD filters use a diffusion coefficient to effectively balance these three aspects [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…The adaptive window anisotropic diffusion (AWAD) [27] filter uses the adaptive window method to calculate the instantaneous coefficient of variation. The weighted Euclidean distance anisotropic diffusion (WEDAD) [21] filter improves the edge detection operator by using weighted Euclidean distances. Other improved AD filters have mainly focused on enhancing edge detection.…”

Section: Introductionmentioning

confidence: 99%

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Speckle Reduction by Directional Coherent Anisotropic Diffusion

et al. 2019

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To effectively balance speckle smoothing and preservation of edges and radiation, a novel anisotropic diffusion filter was developed that uses a directional coherent coefficient. The proposed filter effectively improves the edge detection operator of a traditional anisotropic diffusion filter. The new edge detection operator calculates 16 direction coherence coefficients to avoid the interference of the edge direction. For the diffusion function, the proposed method directly uses the detected directional coherent edge as the diffusion coefficient, which simplifies the calculation of the diffusion function and avoids the adverse effects of inaccurate estimation of the diffusion function threshold for a traditional anisotropic diffusion filter. The influence of the number of iterations and time steps on the proposed filter was analyzed. A series of experiments was conducted with a simulated image and three real synthetic-aperture radar images from different sensors. The results confirmed that the proposed method not only significantly reduces speckle but also effectively preserves the edge and radiation information of images.

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A Novel Decision Mechanism for Image Edge Detection

Jing

Liu

et al. 2021

Intelligent Computing Theories and Application

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Edge detection is one of the most important and fundamental problems in the field of computer vision and image processing. Edge contours extracted from images are widely used as critical cues for various image understanding tasks such as image segmentation, object detection, image retrieval, and corner detection. The purpose of this paper is to review the latest developments on image edge detection. Firstly, the definition and properties of edges are introduced. Secondly, the existing edge detection methods are classified and introduced in detail. Thirdly, the existing widely used datasets and evaluation criteria for edge detection methods are summarized. Finally, future research directions for edge detection are elaborated.

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Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

Cited by 21 publications

References 27 publications

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Speckle Reduction by Directional Coherent Anisotropic Diffusion

A Novel Decision Mechanism for Image Edge Detection

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