ABSTRACT
This paper reviews wavelet-based denoising techniques and presents an effective new algorithm using adaptive threshold method, processing of detailed and approximate coefficient, the optimal choice of level of decomposition for noise reduction in non-stationary signals. The method is based on computing of orthogonal wavelet transform and level-dependent estimation of the threshold. The proposed algorithm is tested with different wavelet bases: Haar, Daubechies; Cubic B-splines; Symplet; Coinflet; biorthogonal wavelets and a comparative analysis is performed. The proposed algorithm was applied to the real signals and has the task of assessing the application of different wavelet bases, threshold techniques, decomposition levels, and times to execute the procedures for accurate and fast-track procedures to reduce interference. The proposed algorithm can be used for single channel real non-stationary signals. For the purpose of comparative analysis of different methods a software program was created by the author, with graphical user interface, that implements the basic denoising techniques, enables the setting of the decomposition level, the wavelet basis, the size of the test signal, and calculates the evaluation characteristics of the denoising process. The software application enables the testing of new denoising algorithms, allows the addition of other types of noise and the investigation of their effects on the signals and is realized in the MATLAB development environment. The proposed results demonstrate that the presented algorithm is suitable for denoising of non-stationary real signals.
- P.Agante and J.P.M.de S'a (1999). ECG Noise Filtering Using Wavelets with Soft-thresholding Methods. In Proc. Comp. in Cardiology'99, 535--542.Google ScholarCross Ref
- S.Chang, B.Yu, M.Vetterli (2000). Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Trans. ImageProcess., 9(9), 1522--1531. Google ScholarDigital Library
- S.A.Chouakri, F.Bereksi-Reguig, S.Ahmaïdi, O.Fokapu (2005). Wavelet Denoising of Electrocardiogram Signal Based on Corrupted Noise Estimation. IEEE Comp. in Card., 32, 1021--1024.Google Scholar
- D.Donoho (1995). Denoising by soft thresholding. IEEE Transactions on Information Theory, 41(3), 613--627. Google ScholarDigital Library
- G.Georgieva-Tsaneva, K.Tcheshmedjiev (2013). Denoising of Electrocardiogram Data with Methods of WA, In: Local Proceed. of 14th Intern. Conf. on Computer Systems and Technologies-CompSysTech'13, University of Ruse, Bulgaria, 9--16.Google Scholar
- E.Gospodinova, M.Gospodinov, N.Dey, I.Domuschiev, A.Ashour, S.Balas, T. Olariu (2016). Specialized Software System for HRV Analysis: An Implementation of Nonlinear Graphical Methods. Soft Computing Applications, Advances in Intelligent Systems and Computing, 633, 367--374.Google ScholarCross Ref
- M.Gospodinov, E.Gospodinova (2007). Comparative analysis of Hurst techniques. CompSysTech '07 Proceed. of 2007 Intern. Conf. on Computer systems and technologies, Bulgaria, Article No63. Google ScholarDigital Library
- L.Garrote, L.Hong, Y.Dong, Z.Yan-sheng (2016). A new wavelet threshold function and denoising application. Mathematical Problems in Engineering.Google Scholar
- H.Madhu, B.Bhavani, S. Sumathi, and H.Vidya (2015). A novel algorithm for denoising of simulated partial discharge signals using adaptive wavelet thresholding methods, in Proc. 2nd Int. Conf. Electron. Commun. Syst., 1596--1602.Google ScholarCross Ref
- S.Mallat, (2009). Denoising. In: A wavelet tour of signal processing: Sparse way, 535-606, Elsevier/Acad. Press, Amsterdam, Boston.Google ScholarCross Ref
- M. Srivastava, C.Anderson, J.Freed (2016). A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds. IEEE Access, 4, 3861--3877.Google ScholarCross Ref
- M.Singh, R.Kumar, A.Kumar (2014). Comparison between different WT and thresholding techniques for ECG denoising. IEEE Intern. Conf. on Advances in Engin. Techn. Research.Google Scholar
Index Terms
- Wavelet Based Interval Varying Algorithm for Optimal Non-Stationary Signal Denoising
Recommendations
Wavelet based image denoising using intra scale dependency
MUSP'06: Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processingThis paper mainly focuses on the development of using wavelet coefficients' intra scale dependency of natural images. Wavelet transform (WT) coefficients have statistical dependency. WT coefficients have dependency between local coefficients (intra-...
Study on Vibration Signal Denoising of Electric Spindle Based on Wavelet Transform
IHMSC '09: Proceedings of the 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 01An testing and processing method of electric spindle status is presented in this article, It is based on wavelet transform. Wavelet transform is selected as its processing platform, it is very important. The theory of wavelet transform and Mallat method ...
A signal denoising algorithm based on overcomplete wavelet representations and Gaussian models
In this paper, we propose a simple signal estimation algorithm based on multiple wavelet representations and Gaussian observation models. The proposed algorithm has two major steps: a joint-optimum estimation of the wavelet coefficients and an averaging ...
Comments