Hybrid Wavelet Transformation and Improved Wavelet Shrinkage Algorithm Method for Reduction of Speckle Noise

2018 ◽  
pp. 45-56
Author(s):  
Mandeep Kaur ◽  
Neeraj Julka ◽  
Satish Saini
Keyword(s):  
Wavelet Transformation ◽  
Speckle Noise ◽  
Wavelet Shrinkage

Wavelet shrinkage generalized Bayes estimation for elliptical distribution parameter’s under LINEX loss

2019 ◽  
Vol 17 (03) ◽  
pp. 1950009 ◽  
Author(s):  
Hamid Karamikabir ◽  
Mahmoud Afshari
Keyword(s):  
Wavelet Transformation ◽  
Test Validity ◽  
Bayes Estimator ◽  
Bayes Estimation ◽  
Multivariate Normal ◽  
Elliptical Distribution ◽  
Wavelet Shrinkage ◽  
Linex Loss ◽  
Generalized Bayes ◽  
Generalized Bayes Estimator

In this paper, the generalized Bayes estimator of elliptical distribution parameter’s under asymmetric Linex error loss function is considered. The new shrinkage generalized Bayes estimator by applying wavelet transformation is investigated. We develop admissibility and minimaxity of shrinkage estimator on multivariate normal distribution.We present the simulation in order to test validity of purpose estimator.

Speckle Noise Reduction in ESPI Fringes Using Wavelet Shrinkage

2000 ◽  
pp. 397-404 ◽  
Author(s):  
Alejandro Federico ◽  
Guillermo H. Kaufmann ◽  
Eduardo P. Serrano
Keyword(s):  
Noise Reduction ◽  
Speckle Noise ◽  
Wavelet Shrinkage ◽  
Speckle Noise Reduction

Wavelet-Based Speckle Noise Reduction in Ultrasound B-Scan Images

2000 ◽  
Vol 22 (2) ◽  
pp. 73-94 ◽  
Author(s):  
Alain Rakotomamonjy ◽  
Philippe Deforge ◽  
Pierre Marché
Keyword(s):  
Ultrasound Image ◽  
Synthetic Data ◽  
Real Data ◽  
Speckle Noise ◽  
Mean Square ◽  
Wavelet Domain ◽  
Least Mean Square ◽  
Wavelet Shrinkage ◽  
B Scan Images ◽  
Speckle Noise Reduction

Speckle noise is known to be signal-dependent in ultrasound imaging. Hence, separating noise from signal becomes a difficult task. This paper describes a wavelet-based method for reducing speckle noise. We derive from the model of the displayed ultrasound image the optimal wavelet-domain filter, in the least mean-square sense. Simulations on synthetic data have been carried out in order to assess the performance of the proposed filter with regards to the classical wavelet shrinkage scheme, while phantom and tissue images have been used for testing it on real data. The results show that the filter effectively reduces the speckle noise while preserving resolvable details.

scholarly journals Speckle Noise Reduction in B-mode Ultrasound Imaging

2011 ◽  
pp. 47-51
Author(s):  
Arun Bhatia
Keyword(s):  
Noise Reduction ◽  
Real Time ◽  
Ultrasound Imaging ◽  
Wavelet Transformation ◽  
Low Cost ◽  
Diagnostic Technique ◽  
Speckle Noise ◽  
Daubechies Wavelet ◽  
Medical Diagnostic ◽  
Speckle Noise Reduction

Ultrasound is a widely used and safe medical diagnostic technique, due to low cost and capability of forming real time imaging. The usefulness of ultrasound imaging is degraded by the presence of signal dependant noise known as speckle. In this paper we make use of daubechies wavelet transformation, Wiener and employing an adaptive thresholding technique in order to improve the performance of this denosing approach the log transformed observation is separated into two images. The summation of these two images constructs the despeckled image.

SAR Image Denoising Algorithm Based on Bayes Wavelet Shrinkage and Fast Guided Filter

2019 ◽  
Vol 23 (1) ◽  
pp. 107-113
Author(s):  
Xiu Jie Yang ◽  
Ping Chen ◽  
◽  
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Speckle Noise ◽  
Guided Filter ◽  
Wavelet Domain ◽  
Wavelet Shrinkage ◽  
Sar Images ◽  
Edge Information ◽  
Posteriori Estimation

To remove the speckle noise of synthetic aperture radar (SAR) images, a novel denoising algorithm based on Bayes wavelet shrinkage and a fast guided filter is proposed. According to the statistical properties of SAR images, the noise-free signal and speckle noise in the wavelet domain are modeled as Laplace and Fisher-Tippett distributions respectively. Then a new wavelet shrinkage algorithm is obtained by adopting the Bayes maximum a posteriori estimation. Speckle noise in the high-frequency domain of SAR images is shrunk by this new wavelet shrinkage algorithm. As the wavelet coefficients of the low-frequency domain also contain some speckle noise, speckle noise in the low-frequency domain can be further filtered by the fast guided filter. The result of the denoising experiments of simulated SAR images and real SAR images demonstrate that the proposed algorithm has the ability to better denoise and preserve edge information.

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