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Least Square based Signal Denoising and Deconvolution using Wavelet Filters

Publication Type : Journal Article

Publisher : Indian Journal of Science and Technology

Source : Indian Journal of Science and Technology, Volume 9, Issue 33, Karpagam College of Engineering (2016)

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Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)

Department : Electronics and Communication

Year : 2016

Abstract : Noise, the unwanted information in a signal reduces the quality of signal. Hence to improve the signal quality, denoising is done. The main aim of the proposed method in this paper is to deconvolve and denoise a noisy signal by least square approach using wavelet filters. In this paper, least square approach given by Selesnick is modified by using different wavelet filters in place of second order sparse matrix applied for deconvolution and smoothing. The wavelet filters used in the proposed approach for denoising are Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Reverse biorthogonal. The result of the proposed experiment is validated in terms of Peak Signal to Noise Ratio (PSNR). Analysis of the experiment results notify that proposed denoising based on least square using wavelet filters are comparable to the performances given by deconvolution and smoothing using the existing second order filter.

Cite this Research Publication : Sowmya, Praveena, R., and Dr. Soman K. P., “Least Square based Signal Denoising and Deconvolution using Wavelet Filters”, Indian Journal of Science and Technology, vol. 9, no. 33, 2016.

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