Shearlet-Based Image Denoising Using Bivariate Shrinkage with Intra-band and Opposite Orientation Dependencies

Guo, Qiang; Yu, Shengsheng; Chen, Xunlei; Liu, Chang; Wei, Wei

doi:10.1109/cso.2009.218

Cited by 12 publications

(20 citation statements)

References 12 publications

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“…However, thresholding in a bivariate MAP that exploits the dependency between coefficients [25,45] gives appropriate results.…”

Section: Parent and Child Coefficient Modelsmentioning

confidence: 99%

“…MI [25,[42][43][44], a parameter for measuring the dependency between X 1 as the child and X 2 as the parent, is expressed as Table 2 Two objective assessment parameters for Barbara to compare with methods in SWT and NSST domains …”

Section: Image Assessment Parametersmentioning

confidence: 99%

“…VISUShrink [19] obtains the universal threshold value whereas SUREShrink [20], BayesShrink [21,22,28], and BiShrink [23][24][25][26]31] determine the adaptive threshold values for every subband. In the following, the speckle noise model is introduced, the BayesShrink and BiShrink methods are explained, and finally, the median for power noise estimation in the transformed domain is expressed.…”

Section: Threshold-based Sar Image Despecklingmentioning

confidence: 99%

“…Regarding the methods, VISUShrink [19] obtains the universal threshold value, whereas SUREShrink [20], BayesShrink [21,22], and bivariate shrinkage (BiShrink) [23][24][25][26] obtain the threshold values adaptively for every subband. Among these approaches, BayesShrink is a well-known method used in the nonsubsampled shearlet transform (NSST) domain [27], and BiShrink functions using the Bayesian estimation theory applied in the wavelet [20,23,28], contourlet [24,29], and shearlet [25] domains are used as well.…”

Section: Introductionmentioning

confidence: 99%

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Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images

Farhangi

Ghofrani

2018

J Image Video Proc.

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Synthetic aperture radar (SAR) images are inherently degraded by multiplicative speckle noise where thresholdingbased methods in the transform domain are appropriate. Being sparse, the coefficients in the transformed domain play a key role in the performance of any thresholding methods. It has been shown that the coefficients of nonsubsampled shearlet transform (NSST) are sparser than those of stationary wavelet transform (SWT) for either clean or noisy images. Therefore, it is expected that thresholding-based methods in NSST outperform those in the SWT domain. In this paper, BayesShrink, BiShrink, weighted BayesShrink, and weighted BiShrink in NSST and SWT domains are compared in terms of subjective and objective image assessment. As BayesShrink try to find the optimum threshold for every subband, BiShrink uses coefficients, name "parent," to clean up coefficients called "child," and the weighted methods consider the coefficients' noise efficiency, which implies that subbands in the transform domain may be affected by noise differently. Two models for considering the parent in the NSST domain are proposed. In addition, for both BayesShrink and BiShrink, considering the weighting factor (coefficients noise efficiency) would improve the performance of the corresponding methods as well. Experimental results show that the weighted-BiShrink despeckling approach in the NSST domain gives an outstanding performance when tested with both artificially speckled images and real SAR images.

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“…However, thresholding in a bivariate MAP that exploits the dependency between coefficients [25,45] gives appropriate results.…”

Section: Parent and Child Coefficient Modelsmentioning

confidence: 99%

Section: Image Assessment Parametersmentioning

confidence: 99%

Section: Threshold-based Sar Image Despecklingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images

Farhangi

Ghofrani

2018

J Image Video Proc.

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“…Additional methods of image denoising based on shearlets were recently presented in the literature [9,13,14,18,46,82,84,92,93] but discussing those in detail would go beyond the scope of this chapter. Many of these methods use variants of the shearlet shrinkage strategy described above, or they introduce some form of adaptivity in the thresholding.…”

Section: Other Shearlet-based Denoising Techniquesmentioning

confidence: 99%

Image Processing Using Shearlets

Easley

Labate

2012

Shearlets

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Since shearlets provide nearly optimally sparse representations for a large class of functions that are useful to model natural images, many image processing methods benefit from their use. In particular, the error rates of data estimation from noise are highly dependent on the sparsity properties of the representation, so that many successful applications of shearlets center around restoration tasks such as denoising and inverse problems. Other imaging problems, where also the application of the shearlet representation turns out to be very beneficial, include image enhancement, image separation, edge detection, and estimation of the geometric features of an object.

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Shearlets

2012

Applied and Numerical Harmonic Analysis

212

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Shearlet-Based Image Denoising Using Bivariate Shrinkage with Intra-band and Opposite Orientation Dependencies

Cited by 12 publications

References 12 publications

Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images

Using BayesShrink, BiShrink, Weighted BayesShrink, and Weighted BiShrink in NSST and SWT for Despeckling SAR Images

Image Processing Using Shearlets

Shearlets

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