NDLI: Softening the multiscale product method for adaptive noise reduction

Content Provider	IEEE Xplore Digital Library
Author	Ge, J. Mirchandani, G.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Vermont Univ., Burlington, VT, USA (Ge, J.; Mirchandani, G.)
Abstract	The goal of denoising is to remove the noise while preserving the important features as much as possible. By exploring the power of parsimonious wavelet basis representation and statistical decision methods, Donoho and Johnstone [1994] pioneered the wavelet shrinkage. However, the performance of traditional wavelet shrinkage is not even as good as that of a simple multiscale product method (MPM) [Y. Xu, et al., 1994], because the wavelet basis representation in the traditional wavelet shrinkage is not shift-invariant. We numerically reveal the connection between the simple MPM [Y. Xu, et al., 1994] and Donoho-Johnstone's hard thresholding [1994]. Based on the observations and an analysis of the MPM, we propose a softened version of MPM which is in analogous to Donoho-Johnstone's soft thresholding [1994]. Thanks to the explicit detection of singularities and the use of both l/sub 2/ and l/sub 0/ stopping criteria to reduce the false detection, the performance of the softened MPM is superior to other methods with redundant wavelet representations for the functions of one-dimensional piecewise linear class. Combined with the local variance analysis discussed elsewhere, we extend the new method to two-dimensional image denoising.
Starting Page	2124
Ending Page	2128
File Size	398066
Page Count	5
File Format	PDF
ISBN	0780381041
DOI	10.1109/ACSSC.2003.1292355
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Softening Noise reduction Wavelet transforms Wavelet coefficients Frequency Least squares approximation Piecewise linear techniques Image analysis Signal analysis Closed-form solution
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Softening the Multiscale Product Method for Adaptive Noise Reduction

Softening the Multiscale Product Method for Adaptive Noise Reduction

Softening the Multiscale Product Method for Adaptive Noise Reduction (2003)

Denoising radar signals using complex wavelet

Nonlinear modeling of wavelet coefficients around edges

Spatially adaptive multiscale thresholding for speckle and mixed noise removal [image denoising]

Image Denoising with an Orientation-Adaptive Gaussian Scale Mixture Model

Robust wavelet denoising

The slantlet transform

Softening the multiscale product method for adaptive noise reduction

Similar Documents

Softening the Multiscale Product Method for Adaptive Noise Reduction

Softening the Multiscale Product Method for Adaptive Noise Reduction

Softening the Multiscale Product Method for Adaptive Noise Reduction (2003)

Denoising radar signals using complex wavelet

Nonlinear modeling of wavelet coefficients around edges

Spatially adaptive multiscale thresholding for speckle and mixed noise removal [image denoising]

Image Denoising with an Orientation-Adaptive Gaussian Scale Mixture Model

Robust wavelet denoising

The slantlet transform

Softening the multiscale product method for adaptive noise reduction