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Method of image filtering using singular decomposition and the surrogate data technology

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Abstract

A method for nonlinear filtering of additive noise on digital image has been proposed. This method is based on presenting the image by its matrix singular decomposition and applying the surrogate data technology to components of the image. The proposed method ensures a superior resolution as compared to most common methods of window filtering that is corroborated by the results of simulation modeling.

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References

  1. R. C. Gonzalez, R. E. Woods, Digital Image Processing, 2nd ed. (Prentice Hall, 2002).

    Google Scholar 

  2. V. T. Fisenko, T. Yu. Fisenko, Computer Image Processing and Image Recognition (SPbGU ITMO, St. Petersburg, 2008) [in Russian].

    MATH  Google Scholar 

  3. I. S. Gruzman, V. S. Kirichuk, Digital Processing of Images in Information Systems (NGTU, Novosibirsk, 2002) [in Russian].

    Google Scholar 

  4. R. Yu. Vitkus, L. P. Yaroslavskii, Adaptive Linear Filters for Image Processing. Adaptive Methods of Image Processing (Nauka, Moscow, 1988) [in Russian, ed. by V. I. Siforov, L. P. Yaroslavskii].

    Google Scholar 

  5. V. I. Solodushkin, V. A. Udod, “One-dimensional image filtering, optimal with respect to resolution,” Atmosph. Oceanic Optics 4, No. 10, 720 (1991), http://ao.iao.ru/en/content/vol.4-1991/iss.10/4.

    Google Scholar 

  6. S. A. Rodionov, “Estimation of optic image quality,” in the Reference Book: Single-Dimensional Computational Optics (Mashinostroyeniye, Leningrad, 1984) [in Russian].

    Google Scholar 

  7. Ya. D. Shirman, Resolution and Compression of Signals (Sov. Radio, Moscow, 1974) [in Russian].

    Google Scholar 

  8. P. Yu. Kostenko, V. I. Vasylyshyn, V. V. Slobodyanyuk, “Additive noise reduction in digital images using the surrogate data technology,” Syst. Obrob. Inf., No. 8, 33 (2014).

    Google Scholar 

  9. H. Kantz, T. Schreiber, Nonlinear Time Series Analysis (University Press, Cambridge, 2004).

    MATH  Google Scholar 

  10. M. Small, Applied Nonlinear Time Series Analysis Applications in Physics, Physiology and Finance (World Scientific Publishing Co. Pte. Ltd., 2005).

    Book  MATH  Google Scholar 

  11. B. Efron, Nonconventional Methods of Multivariate Statistical Analysis (Finansy i Statistika, Moscow, 1988) [in Russian, translation from English of articles collection], 263 p.

    Google Scholar 

  12. P. Yu. Kostenko, K. S. Vasyuta, V. V. Slobodyanyuk, D. S. Yakovenko, “The use of surrogate signals for enhancing the estimation quality of parameters of regular and chaotic signals observed against the background of additive noise,” Systems of Control, Navigation and Communications, No. 4, 28 (2010).

    Google Scholar 

  13. P. Yu. Kostenko, V. I. Vasylyshyn, “Signal processing correction in spectral analysis using the surrogate autocovariance observation functions obtained by the ATS-algorithm,” Radioelectron. Commun. Syst. 57(6), 235 (2014), DOI: 10.3103/S0735272714060016.

    Article  Google Scholar 

  14. P. Yu. Kostenko, V. I. Vasylyshyn, “Enhancing the efficiency of spectral analysis of signals by the Root-MUSIC method using surrogate data,” Radioelectron. Commun. Syst. 57(1), 31 (2014), DOI: 10.3103/S0735272714010026.

    Article  Google Scholar 

  15. V. V. Slobodyanyuk, O. V. Shapovalov, “Analysis of the impact of the scanning type of noisy digital image on the efficiency of noise suppression method using the surrogate data technology,” Syst. Obrob. Inf., No. 5, 22 (2015).

    Google Scholar 

  16. D. L. Danilov, A. A. Zhiglyavskii, Main Components of Time Series: Caterpillar Method (SPbGU, St. Petersburg, 1997) [in Russian].

    Google Scholar 

  17. A. B. Gershman, Johann F. Bohme, “Improved DOA estimation via pseudorandom resampling of spatial spectrum,” IEEE Signal Process. Lett. 4, No. 2, 54 (Feb. 1997), DOI: 10.1109/97.554472.

    Article  Google Scholar 

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Authors and Affiliations

  1. Kharkiv Air Force University, Kharkiv, Ukraine

    P. Yu. Kostenko, V. V. Slobodyanuk & O. V. Plahotenko

Authors
  1. P. Yu. Kostenko
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  2. V. V. Slobodyanuk
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  3. O. V. Plahotenko
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Corresponding author

Correspondence to V. V. Slobodyanuk.

Additional information

Original Russian Text © P.Yu. Kostenko, V.V. Slobodyanuk, O.V. Plahotenko, 2016, published in Izvestiya Vysshikh Uchebnykh Zavedenii, Radioelektronika, 2016, Vol. 59, No. 9, pp. 36–46.

ORCID: 0000-0002-3382-0684

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Kostenko, P.Y., Slobodyanuk, V.V. & Plahotenko, O.V. Method of image filtering using singular decomposition and the surrogate data technology. Radioelectron.Commun.Syst. 59, 409–416 (2016). https://doi.org/10.3103/S0735272716090041

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  • DOI: https://doi.org/10.3103/S0735272716090041

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