SAR Image Despeckling Based on Nonsubsampled Shearlet Transform

Hou, Biao; Zhang, Xiaohua; Bu, Xiaoming; Feng, Hao

doi:10.1109/jstars.2012.2196680

Cited by 101 publications

(47 citation statements)

References 25 publications

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“…In general, the despeckling of SAR images is carried out in either the spatial or transformed domain [1]. Despite their low computational complexity, the performance of spatial domain filters is often not as well as the transformed domain algorithms [2]. Wavelet [3] is a well-known multiscale transform that can effectively mitigate point singularities for onedimensional signals.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, shearlet exhibits highly directional sensitivity and is spatially localized [8][9][10]. ST has been applied in various practical problems such as total variation for denoising [11], deconvolution [12], SAR despeckling [2,13], and Bayesian shearlet shrinkage for SAR despeckling via sparse representation [14]. Further, Markarian and Ghofrani [15] proposed a new method based on compressive sensing for speckle reduction of SAR images.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the coefficients' noise efficiency in SWT and NSST to obtain the weighting factor and the optimum threshold value is the main contribution of this paper. However, the performance of three proposed methods in SWT and four new approaches in NSST are compared with five state-of-the-art papers ( [2,13,15,27,30]) in terms of subjective and objective image evaluations when artificially speckled and real SAR images are denoised.…”

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…One is de-noised methods based on spatial filtering, and the other is de-noised methods based on multi-scale transform. For example, Lee filter (Lee et al, 1994), total variation regularization de-noising methods (Eom, 2011) and non-local means (NLM) de-noising methods (Torres, 2013) are de-noised methods base on spatial filtering; Bayesian wavelet shrinkage with edge detection for SAR image despeckling (Dai et al, 2004), and the SAR image despeckling based on nonsubsampled shearlet transform (Hou et al, 2012) are de-noised methods based on transform domain. In recent years, with the continuous improvement of the theory of multi-scale and multi-resolution transform, the de-noised methods based on transform domain are widely used in SAR image de-noising.…”

Section: Introductionmentioning

confidence: 99%

“…In non-local means (NLM), by making use the repeat patterns existed in natural images (Buades et al, 2005), the gray pixel value is computed by all similar images in the weighted average between a fixed window centered on it and the windows centered on the other pixels in the whole image. By improving collaborative filtering and the compution of de-noised weight, in Dai et al (2004) and Hou et al (2012), non-local mean is extended to SAR image de-noising filed. NLM filter maked use of spatial correlation of the whole image to remove noise and can gain satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

SAR Image De-Noising based on GNL-Means with Optimized Pixel-Wise Weighting in Non-Subsample Shearlet Domain

Liu

Liu²,

Zhao³

2016

CIS

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SAR images have been widely used in many fields such as military and remote sensing. So the suppression of the speckle has been an important research issues. To improve the visual effect of non-local means, generalized non-local (GNL) means with optimized pixel-wise weighting is applied to shrink the coefficients of non-subsample Shearlet transform (NSST) of SAR image. The new method can optimize the weight of GNL, which not only improve the PSNR of de-noised image, but also can significantly enhance the visual effect of de-noising image.

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Multimodal neurological image fusion based on adaptive biological inspired neural model in nonsubsampled Shearlet domain

Singh

Anand

2018

Int J Imaging Syst Tech

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Multimodal medical image fusion (MMIF) has a significant role for a better visualization of the diagnostic statistics, those help the medical professionals in the precise diagnosis of several critical diseases. This paper presents an improved fusion framework that uses the entire features extracted by the nonsubsampled shearlet transform (NSST) and adaptive biologically inspired neural model. The proposed scheme retains the required information without losing the resolution of the disease morphology. In the proposed method, the adaptive neural model based on local visibility and log Gabor energy based rules are applied to low and high-frequency components, respectively. The better fusion results obtained by the proposed approach are confirmed by a large extent of simulations on the different MR-SPECT and CT-MR neurological images. Based on all these simulated results, it states that the proposed approach is superior than the other approaches as it produces better visually fused images with improved computational measures. K E Y W O R D SCT, image fusion, MR, multimodal, SPECT

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SAR Image Despeckling Based on Nonsubsampled Shearlet Transform

Cited by 101 publications

References 25 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

SAR Image De-Noising based on GNL-Means with Optimized Pixel-Wise Weighting in Non-Subsample Shearlet Domain

Multimodal neurological image fusion based on adaptive biological inspired neural model in nonsubsampled Shearlet domain

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