Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
A Dual Tree Complex Wavelet Transform Construction and Its Application to Imagesing
Sathesh, Samuel Manoharan
Pages - 293 - 300     |    Revised - 30-12-2009     |    Published - 31-01-2010
Volume - 3   Issue - 6    |    Publication Date - January 2010  Table of Contents
Complex Discrete Wavelet Transform (CDWT), Dual-Tree, Filter Bank, Shift Invariance, Optimal Thresholding
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.
CITED BY (0)  
1 Google Scholar
2 Academic Index
3 CiteSeerX
4 refSeek
6 Socol@r
7 ResearchGATE
8 Bielefeld Academic Search Engine (BASE)
9 OpenJ-Gate
10 Scribd
11 SlideShare
13 PdfSR
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.
3 S.G. Chang, Y.Bin, and M.vetterli, “Adaptive wavelet thresholding for image denoising and compression”, IEEE Transaction on image processing., Vol.9, No.9, pp.1532 – 1546, Sep.2000.
4 Ming Zhang and Bahadir K. Gunturk, “ Multiresolution Bilateral Filtering for Image Denoising” IEEE Transaction on Image processing ,Vol.17, No.12 Dec. 2008.
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.
7 N. G. Kingsbury,”A dual-tree complex wavelet transform with improved orthogonality and symmetry properties”, In Proceedings of the IEEE Int. Conf. on Image Proc. (ICIP), 2000.
8 J.Scharcanskim C.R.Jung and R.T.Clarke, “Adaptive image denoising using scale and space consistency”, IEEE Transaction on image processing ., Vol.11, No.9,pp.1092 – 1101,Sep.2002.
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.
Mr. Sathesh
- India
Dr. Samuel Manoharan
- India