Skip Nav Destination
ASME Press Select Proceedings
International Conference on Computer and Computer Intelligence (ICCCI 2011)
By
ISBN:
9780791859926
No. of Pages:
740
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
35 Constrained Spectrum Denoising Based on Sparse Representation
By
Chang-Hua Lu
,
Chang-Hua Lu
School of Computer & Information,
Hefei University of Technology
Anhui Institute of Optics and Fine Mechanics,
Chinese Academy of Sciences
Search for other works by this author on:
Guang-Wen Mu
,
Guang-Wen Mu
School of Computer & Information,
Hefei University of Technology
Search for other works by this author on:
Wen-Qing Liu
,
Wen-Qing Liu
Anhui Institute of Optics and Fine Mechanics,
Chinese Academy of Sciences
Search for other works by this author on:
Jian-Guo Liu
,
Jian-Guo Liu
Anhui Institute of Optics and Fine Mechanics,
Chinese Academy of Sciences
Search for other works by this author on:
Yu-Jun Zhang
Yu-Jun Zhang
Anhui Institute of Optics and Fine Mechanics,
Chinese Academy of Sciences
Search for other works by this author on:
Page Count:
5
-
Published:2011
Citation
Lu, C, Mu, G, Liu, W, Liu, J, & Zhang, Y. "Constrained Spectrum Denoising Based on Sparse Representation." International Conference on Computer and Computer Intelligence (ICCCI 2011). Ed. Xie, Y. ASME Press, 2011.
Download citation file:
A method based on a combined use of sparse representation and data fidelity constraints was proposed to remove the noise in the measured spectrum signals. Experiment with signal to noise ratio (SNR), root mean square error (RMSE), and normal correlation coefficient (NCC) three indicators evaluation de-noising performance. Simulation results show that: in comparison with the wavelet denoising methods, the method in this paper has better de-noising ability, and gain better evaluation norm.
Abstract
Key Words
1. Introduction
2. Sparse Representation
3. Maximum a Posteriori Probability Function
4. Experiment and Discussion
5. Summaries
6. Acknowledgment
References
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
Data Analysis of Optimal Square of Pixel Pair Modification Based Brute Force
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
Image Smoothing Based on Game of Life
International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)
Spice Model on High Frequency Vibration for CMUT Application
International Conference on Mechanical and Electrical Technology, 3rd, (ICMET-China 2011), Volumes 1–3
A Simple and Effective Thredshold Setting Approach for Prach Detection in LTE System
International Conference on Information Technology and Computer Science, 3rd (ITCS 2011)
Related Articles
Adaptive Kalman Filtering: A Simulation Result
J. Dyn. Sys., Meas., Control (March,1988)
State Estimation With Finite Signal-to-Noise Models via Linear Matrix Inequalities
J. Dyn. Sys., Meas., Control (March,2007)
Dynamic Model Validation Metric Based on Wavelet Thresholded Signals
J. Verif. Valid. Uncert (June,2017)