SEARCH WITHIN CONTENT
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 4, Pages 1,683-1,700, DOI: https://doi.org/10.21307/ijssis-2017-727
License : (CC BY-NC-ND 4.0)
Received Date : 15-June-2014 / Accepted: 06-November-2014 / Published Online: 01-December-2014
Wireless fading channels exist mixed noise, the most common noises are gaussian noise and impulse noise, to ensure the quality of the received signal, removing the noise in the channel is very important and necessary. WSDN (wavelet soft-threshold de-noising) can suppress low-intensity gaussian noise well; unfortunately, the denoising effect in removing impulse noise using WSDN is not obvious, especially when noise intensity is relatively high. BPDN (Basis Pursuit Denoising) which is a reconstruction algorithm of compressive sensing can control high-intensity gaussian noise in the channels well preceding WSDN, but also the effect of BPDN denoising the common impulse noise is not obvious, to denoise impulse noise, we adopt IBPDN (Improved Basis Pursuit Denoising) that changes L2 form to noise to L1 form, which was proposed in 2006 by Guosheng Bing. The experimental data show that IBPDN has good anti-noise ability to both gaussian noise and impulse noise; furthermore, more satisfactory results are obtained using IBPDN to mixed noise than those with BPDN.
 K.Berkner and R.O. Wells Jr, “Smoothness Estimates for Soft-Threshold Denoising via
Translation-Invariant Wavelet Transforms”, Applied and Computational Harmonic Analysis,
vol. 12, No. 1, 2002, pp. 1-24.
 E.Candes, J.Romberg and T.Tao, “Robust Uncertainty Principles: Exact Signal
Reconstruction from Highly Incomplete Frequency Information”, IEEE Transactions on
Information Theory, vol. 52, no. 2, pp. 489-509, February 2006.
 D.L. Donoho, “Compressed Sensing”, IEEE Transactions on Information Theory, vol. 52,
no. 4, pp. 1289-1306., April 2006.
 E.Candes, J.Romberg and T.Tao, “Stable Signal Recovery from Incomplete and Inaccurate
Measurements”, Communications on Pure and Applied Mathematics, vol. 59, no. 8, pp.
1207-1223, August 2006.
 E.Candes, “Compressive Sampling”, Proceedings of the International Congress of
Mathematicians, Madrid, Spain, 3(2006), 1433-1452.
 S.Malla, Z.Zhang, “Matching Pursuits with Time Frequency Dictionaries”, IEEE
Transactions on Signal Process, vol. 41, No. 12, 1993, pp. 3397-3415.
 S.Satpathi, R.L.Das, M.Chakraborty, “Improving the Bound on the RIP Constant in
Generalized Orthogonal Matching Pursuit”, IEEE Signal Processing Letters, vol. 20, No. 11,
pp. 1074-1077, November 2013.
 S.S. Chen, D.L. Donoho, M.A. Saunders, “Atomic Decomposition by Basis Pursuit”, SIAM
Journal on Scientific Computing, vol. 20, No. 1, 1998, pp. 33-61.
W.Lu and N.Vaswani, “Regularized Modified BPDN for Noisy Sparse Reconstruction with
Partial Erroneous Support and Signal Value Knowledge”, IEEE Transactions on Signal
Processing, vol.69, no.1, pp. 182-196, January 2012.
 W.Lu and N.Vaswani, “Modified Basis Pursuit Denoising (modified-bpdn) for Noisy
Compressive Sensing with Partially Known Support”, IEEE International Conference
Acoustics, Speech, Signal Processing. (ICASSP), 2010.
 J.L. Starck, M. Elad and D.L. Donoho, “Image Decomposition via The Combination of
Sparse Representations and AVariational Approach,” IEEE Transactions on Image Processing,
Vol .14, no.10, pp. 1570-1582, October 2005.
 G.S.Bing, L.Wang, W.B.Tian, “An Improved Compress Sensing’s Signal Reconstruction
Algorithm Based on Basis Pursuit Denoising”, Electronic Measurement Technology, 2010.
 E.Candes and T.Tao, “Decoding by Linear Programming”, IEEE Transactions. on
Information Theory, Vol .51, no.12, pp. 4203-4203, December 2005.
 J. Feldman, “Decoding Error-Correcting Codes via Linear Programming,” Ph.D.
dissertation, Dept. of Electrical Engineering and Computer Science, Massachusetts Institute of
Technology, Cambridge, MA, 2003.
 J. Feldman, M. J. Wainwright, and D. R. Karger, “Using Linear Programming to Decode
Binary Linear Codes”, IEEE Transactions on Information Theory, Vol .51, no.3, pp. 954-972,
 Biqiang Du, Ning Xi, Erick Nieves, “Industrial Robot Calibration Using a Virtual Linear
Constraint”, International Journal On Smart Sensing and Intelligent Systems, vol. 5, No. 4, pp.
987-1002, December 2012.
 G.M Shi, D.H.Liu, D.H.Gao, “Compressed Sensing Theory and Its Research Progress”,
Chinese Journal of Electronics, vol. 37, No. 5, 2009, pp. 1070-1081.
 E. Candes, J. Romberg, “Practical Signal Recovery from Random Projections”, http :
/www. acm. caltech. edu/ ~emmanuel/ papers/PracticalRecovery. Pdf.
 H.C Zhou, “Research on Methods for Sparse Parameter Model and Parameter Prior
Based on Image Resolution Enhancement”, Chang Sha:National University of Defense
 P.Bloomfield, W.Steiger, “Least Absolute Deviations:Theory, Applications, and
Algorithms.Boston : Birkhauser”, 1983.