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.

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.

DAUBECHIES COMPLEX WAVELET TRANSFORM BASED TECHNIQUE FOR DENOISING OF MEDICAL IMAGES

2007 ◽  
Vol 07 (04) ◽  
pp. 663-687 ◽  
Author(s):  
ASHISH KHARE ◽  
UMA SHANKER TIWARY
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Medical Image ◽  
Medical Images ◽  
Universal Method ◽  
Complex Wavelet Transform ◽  
Shrinkage Function ◽  
Daubechies Complex Wavelet Transform ◽  
Complex Wavelet

Wavelet based denoising is an effective way to improve the quality of images. Various methods have been proposed for denoising using real-valued wavelet transform. Complex valued wavelets exist but are rarely used. The complex wavelet transform provides phase information and it is shift invariant in nature. In medical image denoising, both removal of phase incoherency as well as maintaining the phase coherency are needed. This paper is an attempt to explore and apply the complex Daubechies wavelet transform for medical image denoising. We have proposed a method to compute a complex threshold, which does not depend on any assumed model of noise. In this sense this is a "universal" method. The proposed complex-domain shrinkage function depends on mean, variance and median of wavelet coefficients. To test the effectiveness of the proposed method, we have computed the input and output SNR and PSNR of various types of medical images. The method gives an improvement for Gaussian additive, Speckle and Salt-&-Pepper noise as well as for the mixture of these noise types for a range of noisy images with 15 db to 30 db noise levels and outperforms other real-valued wavelet transform based methods. The application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in the experiments.

scholarly journals SNR Improvement for Evoked Potential Estimation using Bi – Orthogonal Wavelet Transform

IJARCCE ◽  
2015 ◽  
Vol 4 (8) ◽  
pp. 408-413
Author(s):  
Shailesh M L ◽  
Dr. Anand Jatti ◽  
Madhushree K S ◽  
Siddesh M B
Keyword(s):  
Wavelet Transform ◽  
Evoked Potential ◽  
Orthogonal Wavelet ◽  
Snr Improvement ◽  
Evoked Potential Estimation ◽  
Orthogonal Wavelet Transform

Orthogonal Wavelet Transform Blind Equalization Algorithm Based on the Optimization of Artificial Fish Swarm Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1399-1402
Author(s):  
Wei Huang ◽  
Ye Cai Guo
Keyword(s):  
Wavelet Transform ◽  
Convergence Rate ◽  
Blind Equalization ◽  
Weight Vector ◽  
Orthogonal Wavelet ◽  
Artificial Fish Swarm Algorithm ◽  
Equalization Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Orthogonal Wavelet Transform

According to disadvantages of big steady-state error, low convergence rate, and local convergence of traditional Constant Modulus blind equalization Algorithm (CMA), an orthogonal Wavelet Transform blind equalization Algorithm based on the optimization of Artificial Fish Swarm Algorithm(AFSA-WT-CMA) is proposed. In this proposed algorithm, the weight vector of the blind equalizer is regarded as artificial fish, the equalizer weight vector can be optimized via making full use of global search and information sharing mechanism of artificial fish school algorithm, the de-correlation ability of normalizing orthogonal wavelet transform. The computer simulations in underwater acoustic channels indicate that the proposed algorithm outperforms CMA and WT-CMA in convergence rate and mean square error.

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