The Wavelet Digest Homepage
Return to the homepage
Search the complete Wavelet Digest database
Help about the Wavelet Digest mailing list
About the Wavelet Digest
The Digest The Community
 Latest Issue  Back Issues  Current submissions  New submission  Events  Gallery
The Wavelet Digest
   -> Volume 13, Issue 4


Thesis: Complex Wavelet Transforms And Their Applications
 
images/spacer.gifimages/spacer.gif Reply into Digest
Previous :: Next  
Author Message
Pancham Shukla (spancham@yahoo.com)
Guest





PostPosted: Fri Jul 08, 2005 11:24 am    
Subject: Thesis: Complex Wavelet Transforms And Their Applications
Reply with quote

PhD Thesis: Complex Wavelet Transforms And Their Applications

URL: http://www.commsp.ee.ic.ac.uk/~pancham/publications.html

ABSTRACT:
Standard DWT (Discrete Wavelet Transform), being non-redundant, is a very powerful tool for many non-stationary Signal Processing applications, but it suffers from three major limitations; 1) shift sensitivity, 2) poor directionality, and 3) absence of phase information. To reduce these limitations, many researchers developed real-valued extensions to the standard DWT such as WP (Wavelet Packet Transform), and SWT (Stationary Wavelet Transform). These extensions are highly redundant and computationally intensive. Complex Wavelet Transform (CWT) is also an alternate, complex-valued extension to the standard DWT. The initial motivation behind the development of CWT was to avail explicitly both magnitude and phase information. This thesis presents a detailed review of Wavelet Transforms (WT) including standard DWT and its extensions. Important forms of CWTs; their theory, properties, implementation, and potential applications are investigated in this thesis. Recent developments in CWTs are classified into two important classes first is, Redundant CWT (RCWT), and second is Non-Redundant CWT (NRCWT). The important forms of RCWT include Kingsbury’s and Selesnick’s Dual-Tree DWT (DT-DWT), whereas the important forms of NRCWT include Fernandes’s and Spaendonck’s Projection based CWT (PCWT), and Orthogonal Hilbert transform filterbank based CWT (OHCWT) respectively. All recent forms of CWTs try to reduce two or more limitations of standard DWT with limited (or controllable) redundancy, or without any redundancy. Potential applications such as Motion estimation, Image fusion/registration, Denoising, Edge detection, and Texture analysis are suggested for further investigation with RCWT. Directional and phase based Compression is suggested for investigation with NRCWT. Denoising and Edge detection applications are investigated with DT-DWTs. Promising results are compared with other DWT extensions, and with the classical approaches. After thorough investigations, it is proposed that by employing DT-DWT for Motion estimation and NRCWT for Compression might significantly improve the performance of the next generation video codecs.
All times are GMT + 1 Hour
Page 1 of 1

 
Jump to: 
 


disclaimer - webmaster@wavelet.org
Powered by phpBB

This page was created in 0.025999 seconds : 18 queries executed : GZIP compression disabled