Local Adaptive Bivariate Shrinkage Function for Seisogram Wavelet Based Denoising

2021 ◽  
Vol 19 (02) ◽  
pp. 342-348
Author(s):  
Yanet Cesaire Velazquez ◽  
Rafael Arturo Trujillo Codorniu
Keyword(s):  
Shrinkage Function ◽  
Bivariate Shrinkage

An Image De-Noising Algorithm Based on the Dual-Tree Complex Wavelet

2011 ◽  
Vol 403-408 ◽  
pp. 1412-1415
Author(s):  
Yong Min Ning ◽  
Ling Ling Li ◽  
Hua Shi ◽  
Jing Chen
Keyword(s):  
Wavelet Transform ◽  
Invariance Property ◽  
Threshold Strategy ◽  
Complex Wavelet Transform ◽  
Shrinkage Function ◽  
Parents And Children ◽  
Shift Invariance ◽  
Complex Wavelet ◽  
Bivariate Shrinkage ◽  
Visual Results

In this paper, an image de-noising algorithm based on the Dual-Tree Complex Wavelet Transform (DT-CWT) is proposed, which takes the advantage of redundant coefficients transformed by DT-CWT. The model of bivariate shrinkage function is used to provide a nonlinear threshold strategy. It exploits the dependency between inter-scale parents and children coefficients to recover the original coefficients more accurate. With the shift-invariance property of DT-CWT coefficients, the algorithm prevents the Gibbs effect caused by the thresholding, which further improves the reconstructed quality. Experiment results show that the de-noised image using DT-CWT can achieve more than 1dB prior to DWT with impressive visual results.

Bi-ComForWaRD: BIVARIATE COMPLEX FOURIER-WAVELET REGULARIZED DECONVOLUTION FOR MEDICAL IMAGING

2009 ◽  
Vol 08 (02) ◽  
pp. 85-95 ◽  
Author(s):  
M. VENU GOPALA RAO ◽  
S. VATHSAL
Keyword(s):  
Wavelet Transform ◽  
Medical Imaging ◽  
Wiener Filter ◽  
Low Complexity ◽  
Complex Wavelet Transform ◽  
Orthogonal Wavelet ◽  
Step Process ◽  
Shrinkage Function ◽  
Bivariate Shrinkage ◽  
Orthogonal Wavelet Transform

In this paper, we propose a new hybrid Bivariate Complex Fourier Wavelet Regularized Deconvolution (Bi-ComForWaRD) that is an extension to the ComForWaRD algorithm, for medical imaging. This new algorithm is a two-step process, a global blur compensation using generalized Wiener filter and followed by a denoising algorithm using local adaptive Bivariate shrinkage function. It is a low-complexity denoising algorithm using the joint statistics of the wavelet coefficients and considers the statistical dependencies between the coefficients. And also, the performance of this system will be demonstrated on both the orthogonal wavelet transform and the dual-tree complex wavelet transform (DT-CWT) and some comparisons with the best available wavelet-based image denoising results will be given in order to illustrate the effectiveness of the system.

DE-NOISING OF CT IMAGES USING COMBINED BIVARIATE SHRINKAGE AND ENHANCED TOTAL VARIATION TECHNIQUE

2018 ◽  
Vol 8 (4) ◽  
pp. 12
Author(s):  
DEVANAND BHONSLE ◽  
VIVEK KUMAR CHANDRA ◽  
SINHA G. R. ◽  
◽  
◽  
...  
Keyword(s):  
Total Variation ◽  
Ct Images ◽  
Bivariate Shrinkage ◽  
Variation Technique
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