Signal Denoising with Interval Dependent Thresholding using DWT and SWT
Ramesh Kumar1, Prabhat Patel2
1Mr. Ramesh Kumar, Electronics and Communication, Jabalpur Engineering College, Jabalpur, India.
2Dr. Prabhat Patel, Electronics and Communication, Jabalpur Engineering College, Jabalpur, India.
Manuscript received on 15 November 2012 | Revised Manuscript received on 25 November 2012 | Manuscript Published on 30 November 2012 | PP: 47-50 | Volume-1 Issue-6, November 2012 | Retrieval Number: E0328101612/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Degradation of signals by noise is an omnipresent problem in almost all fields of signal processing. Therefore, in practical applications, before analyzing the received signal it is necessary to de-noise it. In this paper we have used the interval dependent threshold selection rule for threshold calculation and analyzed its performance with the help of Discrete and Stationary Wavelet Transform. The results show that Mean Square Error (MSE) of the interval dependent threshold selection rule is less than that of conventional fix form threshold selection rule for signal de-noising. Moreover using the interval dependent thresholding with Stationary Wavelet Transform (SWT) the denoising capacity of the SWT increases significantly. Therefore, it proves the superiority of proposed signal denoising algorithm. Moreover the result obtained with is compared with the denoising capability of interval dependent threshold selection rule using Discrete Wavelet Transform(DWT) and it is found that SWT gives better performance than DWT due to its property of translational invariance.
Keywords: Interval dependent threshold selection, Discrete Wavelet Transform (DWT). Stationary Wavelet Transform (SWT). Mean Square Error (MSE), Signal Denoising.
Scope of the Article: Discrete Wavelet Transform