NDLI: The analysis of noise reduction performance in Compressed Sensing

Content Provider	IEEE Xplore Digital Library
Author	Ning Wan-zheng Wang Hai-yan Wang Xuan Yang Fu-zhou
Copyright Year	2011
Description	Author affiliation: College of Electronic Information, Northwest Polytechnical University, Xi'an, China (Wang Xuan) \|\| College of Marine Engineering, Northwest Polytechnical University, Xi'an, China (Ning Wan-zheng; Wang Hai-yan; Yang Fu-zhou)
Abstract	For the Compressed Sensing (CS) problem of noisy signal, the observation model of signal with white Gaussian noise is established and the changes of measurements caused by noise are studied. It is found that, the sparse manifestation of signal and the process of obtaining measurements have the function of noise reduction. In computer simulation, Gaussian random matrix and Orthogonal Matching Pursuit (OMP) algorithm are used to calculate the trend of reconstruction error along with the number of measurements in different Signal to Noise Ratio (SNR). The results of simulation show that in a certain range of SNR, there are always a certain number of measurements to make the error of reconstructed signal less than the error of noisy signal itself. At last, the empirical formula among Matching Rate, SNR and the number of measurements is fitted by experimental data, which contributes to the selection of optimal number of measurements.
Starting Page	1
Ending Page	5
File Size	298678
Page Count	5
File Format	PDF
ISBN	9781457708930
e-ISBN	9781457708947
DOI	10.1109/ICSPCC.2011.6061622
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Matching Rate Empirical formula Measurement uncertainty Noise reduction Matching pursuit algorithms Compressed Sensing Noise measurement Sparse matrices Signal to noise ratio
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

Analysis and extension of existing bounds in compressed sensing

Support Recovery With Orthogonal Matching Pursuit in the Presence of Noise

Analysis on greedy reconstruction algorithms based on compressed sensing

Block sparse signal recovery from sparsely corrupted measurements

Forward-backward search for compressed sensing signal recovery

Greedy Orthogonal Matching Pursuit algorithm for sparse signal recovery in compressive sensing

Performance analysis of compressive sensing reconstruction

Rate-Distortion Bounds for Sparse Approximation

SegBOMP: An efficient algorithm for block non-sparse signal recovery

The analysis of noise reduction performance in Compressed Sensing

Similar Documents

Analysis and extension of existing bounds in compressed sensing

Support Recovery With Orthogonal Matching Pursuit in the Presence of Noise

Analysis on greedy reconstruction algorithms based on compressed sensing

Block sparse signal recovery from sparsely corrupted measurements

Forward-backward search for compressed sensing signal recovery

Greedy Orthogonal Matching Pursuit algorithm for sparse signal recovery in compressive sensing

Performance analysis of compressive sensing reconstruction

Rate-Distortion Bounds for Sparse Approximation

SegBOMP: An efficient algorithm for block non-sparse signal recovery

The analysis of noise reduction performance in Compressed Sensing