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Least Square based Signal Denoising and Deconvolution using Wavelet Filters

Affiliations

  • Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, India

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.

Keywords

Least Square, Peak Signal to Noise Ratio (PSNR), Signal Denoising, Wavelet Filters

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References


  • Thomas S, Velayudhan A, Baby N, Peter S. Study of ECG signal denoising and peak detection techniques. Journal of Network Communications and Emerging Technologies. 2015 Aug; 3(2):1–3.
  • Joshi SL, Vatti RA, Tornekar RV. A survey on ECG signal denoising techniques.Communication Systems and Network Technologies, 2013 International Conference;2013 Apr. p. 60–4,
  • Bruni V, Vitulano D. Wavelet-based signal de-noising via simple singularities approximation. Signal Processing. 2006 Apr; 86(4):859–76.
  • Rioul O, Vetterli M. Wavelets and signal processing. IEEE Signal Processing Magazine. 1991Oct; 8(4):14–38.
  • Chen G, Bui T. Multi wavelets denoising using neighboring coefficients. Signal Processing Letters. 2003 Jul; 10(7):211–14.
  • Crouse MS, Nowak RD, Baraniuk RG. Wavelet-based statistical signal processing using hidden Markov models. IEEE Transactions onSignal Processing. 1998 Apr; 46(4):886–902.
  • Soman K. Insight into wavelets: from theory to practice. Prentice Hall India Learning Private Limited, 3rd edition; 2010.
  • Jensen A, La Cour-Harbo A. Ripples in mathematics: the discrete wavelet transform. Springer Science and Business Media; 2001. p. 51–60.
  • Debnath L. Brief historical introduction to wavelet transforms. International Journal of Mathematical Education in Science and Technology. 1998; 29(5):677–88.
  • Selesnick I. Least squares with examples in signal processing. Open Stax-CNX. Module:m46131; 2013 Apr. p. 1–25.
  • Aarthy G, Amitha P, Krishnan T, Pillai S, Sowmya V, Soman K. A comparative study of spike and smooth separation from a signal using Different over complete dictionary. 2013 International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s); 2013 Mar. p. 590–5.

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