A new SAR image despeckling using directional smoothing filter and method noise thresholding

Singh, Prabhishek; Shree, Raj

doi:10.1016/j.jestch.2018.05.009

Cited by 40 publications

(30 citation statements)

References 51 publications

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“…where k => 8000 r is the correlation coefficient. The greatest standard deviation estimate is received when r value is close to zero, then σ r ≈ 0.011 where r ≈ 0.5 we have σ r ≈ 0.007 [29][30][31][32][33][34][35][36][37][38][39][40][41].…”

Section: Covariance Model Of Vibration Signal Parametersmentioning

confidence: 94%

Analysis of Dynamic Parameters of a Railway Bridge

et al. 2019

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This article analyses the dispersion of vibration accelerations of a railway bridge during the passage of a train, and presents an analysis of their parameters after the application of the theory of covariance functions. The measurements of vibration accelerations at the fixed points of the beams of the overlay of the bridge were recorded in the time scale as digital arrays (matrices). The values of inter-covariance functions of the arrays of data of measurements of digital vibration accelerations and the values of auto-covariance functions of the individual arrays, changing the quantization interval in the time scale, were calculated. The compiled software Matlab 7 in the operator package environment was used in calculations. This article aims at determining the interdependencies of results of vibrations of bridge points rather than at the impact which a train makes on a bridge without emphasizing the modal parameters of the bridge. The aforementioned interdependencies make it possible to predict the results of hard-to-reach points.

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Section: Covariance Model Of Vibration Signal Parametersmentioning

confidence: 94%

Analysis of Dynamic Parameters of a Railway Bridge

et al. 2019

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“…Therefore, the actual SAR images are not possible to test the algorithm at different noise variances. For this purpose, the concept of simulated SAR images is introduced [43]. Furthermore, a simulated SAR image is evaluated by PSNR, SNR, SSIM, and MAE (Table 10).…”

Section: Computational Complexitymentioning

confidence: 99%

A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

et al. 2020

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The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.

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“…However, the SRAD filtering result image still includes the speckle noise, which represents a form of multiplicative noise. Since most of the filtering methods are developed for reducing the AGWN, the logarithmic transform is applied to the resulting SRAD image to convert the multiplicative noise into additive noise [41], after which the resulting SRAD image contains additive noise. Subsequently, the two-dimensional (2D) DWT transforms the SRAD filtering result image, which represents the logarithmic transform, into four sub-band images (vertical sub-band image (LH), horizontal sub-band image (HL), diagonal sub-band image (HH), and approximate sub-band image (LL)).…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…We used peak signal-to-noise (PSNR), structural similarity (SSIM), and equivalent number of looks (ENL) to compare the performance of speckle noise reduction in the SAR images [41]. The PSNR depicts the maximum signal-to-noise ratio.…”

Section: Evaluation Metricsmentioning

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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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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A new SAR image despeckling using directional smoothing filter and method noise thresholding

Cited by 40 publications

References 51 publications

Analysis of Dynamic Parameters of a Railway Bridge

Analysis of Dynamic Parameters of a Railway Bridge

A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

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

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