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Wavelets and adaptive thresholding

  • Mathematical Models, Computational Methods
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Abstract

The discrete wavelet transform and its application for signal denoising is considered. The article is oriented to readers unfamiliar with the wavelet theory and, therefore, basic definitions and theorems required for the understanding of the material below are presented at the beginning of the article. Having read this article, the reader becomes acquainted with the wavelet theory and learns the signal denoising method that is based on the wavelet transform.

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References

  1. I. Daubechies, Ten Lectures on Wavelets (SIAM, Philadelphia, 1992; NITS RKhD, Izhevsk-Moscow, 2001).

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  2. N. K. Smolentsev, Wavelet Analysis in MATLAB (DMK Press, Moscow, 2010) [in Russian].

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  3. D. L. Donoho and M. Iain, “Johnstone Adapting to Unknown Smoothness via Wavelet Shrinkage,” J. Am. Stat. Ass. 90(432), 1200–1224 (1995).

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Authors and Affiliations

  1. Moscow Institute of Physics and Technology, State University, Institutskii per. 9, Dolgoprudnyi, Moscow oblast, 141700, Russia

    M. V. Obidin

  2. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Bol’shoi Karetnyi per. 19, Moscow, 127994, Russia

    A. P. Serebrovski

Authors
  1. M. V. Obidin
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  2. A. P. Serebrovski
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Corresponding author

Correspondence to A. P. Serebrovski.

Additional information

Original Russian Text © M.V. Obidin, A.P. Serebrovski, 2013, published in Informatsionnye Protsessy, 2013, Vol. 13, No. 2, pp. 91–99.

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Obidin, M.V., Serebrovski, A.P. Wavelets and adaptive thresholding. J. Commun. Technol. Electron. 59, 1434–1439 (2014). https://doi.org/10.1134/S106422691412016X

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  • DOI: https://doi.org/10.1134/S106422691412016X

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