NDLI: Wavelet denoising via sparse representation

Content Provider	Springer Nature Link
Author	Zhao, RuiZhen Liu, XiaoYu Li, Ching Chung Sclabassi, Robert J. Sun, MinGui
Copyright Year	2009
Abstract	Wavelet threshold denoising is a powerful method for suppressing noise in signals and images. However, this method often uses a coordinate-wise processing scheme, which ignores the structural properties in the wavelet coefficients. We propose a new wavelet denoising method using sparse representation which is a powerful mathematical tool recently developed. Instead of thresholding wavelet coefficients individually, we minimize the number of non-zero coefficients under certain conditions. The denoised signal is reconstructed by solving an optimization problem. It is shown that the solution to the optimization problem can be obtained uniquely and the estimates of the denoised wavelet coefficients are unbiased, i.e., the statistical means of the estimates are equal to the noise-free wavelet coefficients. It is also shown that at least a local optimal solution to the denoising problem can be found. Our experiments on test data indicate that this new denoising method is effective and efficient for a wide variety of signals including those with low signal-to-noise ratios.
Starting Page	1371
Ending Page	1377
Page Count	7
File Format	PDF
ISSN	10092757
Journal	Science in China Series : Information Sciences
Volume Number	52
Issue Number	8
e-ISSN	18622836
Language	English
Publisher	SP Science in China Press
Publisher Date	2009-07-02
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	signal processing denoising sparse representation wavelet transform Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Computer Science

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

Construction of a new adaptive wavelet network and its learning algorithm

Image denoising using sparse representation classification and non-subsampled shearlet transform

A New Method for Sparse Signal Denoising Based on Compressed Sensing

Diffusion filtering in image processing based on wavelet transform

Bilateral signal variance estimation for wavelet-domain image denoising

Ways to sparse representation: An overview

A region-based easy-path wavelet transform for sparse image representation

Study on Vibration Signal Denoising of Electric Spindle Based on Wavelet Transform

Denoising and baseline correction of ECG signals using sparse representation

Wavelet denoising via sparse representation

Similar Documents

Construction of a new adaptive wavelet network and its learning algorithm

Image denoising using sparse representation classification and non-subsampled shearlet transform

A New Method for Sparse Signal Denoising Based on Compressed Sensing

Diffusion filtering in image processing based on wavelet transform

Bilateral signal variance estimation for wavelet-domain image denoising

Ways to sparse representation: An overview

A region-based easy-path wavelet transform for sparse image representation

Study on Vibration Signal Denoising of Electric Spindle Based on Wavelet Transform

Denoising and baseline correction of ECG signals using sparse representation

Wavelet denoising via sparse representation