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A Dual Tree Complex Wavelet Transform Construction and Its Application to Imagesing
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International Journal of Image Processing (IJIP)
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Volume:  3    Issue:  6
Pages:  265-384
Publication Date:   January 2010
ISSN (Online): 1985-2304
Pages 
293 - 300
Author(s)  
Sathesh - India
 
Published Date   
31-01-2010 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
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Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Complex Discrete Wavelet Transform (CDWT), Dual-Tree, Filter Bank, Shift Invariance, Optimal Thresholding 
 
 
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This paper discusses the application of complex discrete wavelet transform (CDWT) which has significant advantages over real wavelet transform for certain signal processing problems. CDWT is a form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. The paper is divided into three sections. The first section deals with the disadvantage of Discrete Wavelet Transform (DWT) and method to overcome it. The second section of the paper is devoted to the theoretical analysis of complex wavelet transform and the last section deals with its verification using the simulated images. 
 
 
 
1 R.Anderson, N.Kinsbury, and J. Fauqueur. ‘Determining Multiscale Image feature angles from complex wavelet phases’ In international conference on Image processing (ICIP), September 2005
2 C. Kervrann and J.Boulanger, “ Optimal spatial adaptation for patch based image denoising,” IEEE Trans. Image Process., Vol. 15, no.10, pp. 2866 – 2878, Oct. 2006.
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5 N. G. Kingsbury,” The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters”, In the Proceedings of the IEEE Digital Signal Processing Workshop, 1998.
6 N. G. Kingsbury,”Image processing with complex wavelets”, Phil. Trans. Royal Society London – Ser. A., vol.357, No.1760, pp. 2543 – 2560,Sep 1999.
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9 J. Neumann and G. Steidl, ”Dual–tree complex wavelet transform in the frequency domain and an application to signal classification”, International Journal of Wavelets, Multiresolution and Information Processing IJWMIP, 2004.
10 K.Hirakawa and T.W. Parks, “Image denoising using total least squares,” IEEE Trans. Process., vol. 15, No. 9, pp. 2730 – 2742, Sep. 2006.
11 J. K. Romberg, H. Choi, R. G. Baraniuk, and N. G. Kingsbury,”Hidden Markov tree models for complex wavelet transforms”, Tech. Rep., Rice University, 2002.
12 L. Sendur and I.W. Selesnick, “ Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency,” IEEE Transactions on signal processing, vol.50, no,11,pp.2744-2756, November 2002.
 
 
 
 
 
 
 
 
Sathesh : Colleagues
Samuel Manoharan : Colleagues  
 
 
 
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