Please use this identifier to cite or link to this item:
Title: Wavelet singularity detection for image processing
Authors: Lun, DPK 
Hsung, TC
Ho, YF
Keywords: Data compression
Image coding
Image denoising
Wavelet transforms
Issue Date: 2002
Publisher: IEEE
Source: The 2002 45th Midwest Symposium on Circuits and Systems, 2002 : MWSCAS-2002, 4-7 August 2002, v. 2, p. II156-II159 How to cite?
Abstract: The idea of wavelet singularity detection (WSD) can be traced back to the work of Jaffard. He showed that the local regularity of an n-dimensional signal (which is measured through its Lipschitz exponent) can be estimated by analyzing its n+1-dimensional scale-space. Mallat further showed that the Lipschitz exponent of a singularity can be estimated by tracing its wavelet transform modulus maxima (WTMM). Nevertheless, the tracing of WTMM is not just a tedious procedure computationally; ambiguity often results from determining the correspondence of a modulus maximum to a singularity. In that light, the wavelet transform modulus sum (WTMS) approach was proposed. In this paper, the applications of WTMS in image denoising, compressed image deblocking, and scalable image coding are described. They show that WSD is a valuable tool for image processing and has widespread applications.
ISBN: 0-7803-7523-8
DOI: 10.1109/MWSCAS.2002.1186821
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Citations as of Aug 13, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.