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Telecommunications and Radio Engineering
SJR: 0.202 SNIP: 0.2 CiteScore™: 0.23

ISSN Imprimir: 0040-2508
ISSN On-line: 1943-6009

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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v78.i13.10
pages 1129-1142


S. K. Abramov
Department of Transmitters, Receivers and Signal Processing, National Aerospace University (Kharkiv Aviation Institute), 17 Chkalov St., Kharkiv, 61070, Ukraine
V. V. Abramova
National Aerospace University (Kharkiv Aviation Institute), 17, Chkalov St., Kharkiv, 61070, Ukraine
V. V. Lukin
National Aerospace University (Kharkiv Aviation Institute), 17 Chkalov St., Kharkiv, 61070, Ukraine
Karen O. Egiazarian
Tampere University, Tampere, 33720, Finland


Signals acquired by different sensors are often noisy and are subject to filtering aimed to reduce noise and preserve important information. Although a great number of different filters exist, their performance does not always satisfy users. There are practical situations when denoising does not lead to expected positive effect which makes it useless. In this paper, we show that a denoising efficiency can be predicted for DCT-based filters. This can be accurately done by an analysis of statistics of DCT-coefficients in a limited number of blocks without execution of denoising itself. Peculiarities of preliminary analysis needed to carry out prediction are discussed. It is shown that a prediction is able to perform well for a wide range of signals and signal-to-noise ratios.


  1. Gold, B., Ellis, D., Morgan, N., Bourlard, H., and Fosler-Lussier, E., (2011) Speech and Audio Signal Processing: Processing and Perception of Speech and Music, USA: Wiley-Interscience, 688 p.

  2. Nielsen, R.O., (1990) Sonar Signal Processing, USA: Artech House, 520 p.

  3. Viunytskyi, O. and Shulgin, V., (2017) Signal processing techniques for fetal electrocardiogram extraction and analysis, Proc. of 2017 IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO), Kyiv, Ukraine, pp. 325-328.

  4. Haykin, S. and Widrow, B. (eds.), (2003) Least-Mean-Square Adaptive Filters, Hoboken, NJ: Wiley- Interscience, 497 p.

  5. Gotchev, A., Nikolaev, N., and Egiazarian, K., (2001) Improving the transform domain ECG denoising performance by applying interbeat and intra-beat decorrelating transforms, Proc. of ISCAS 2001, Sydney, NSW, 2, pp. 17-20, doi: 10.1109/ISCAS.2001.920995.

  6. Astola, J. and Kuosmanen, P., (1997) Fundamentals of nonlinear digital filtering, Boca Raton (USA): CRC Press LLC, 288 p.

  7. Hwang, H. and Haddad, R.A., (1995) Adaptive median filters: new algorithms and results, J. IEEE Transactions on Image Processing, 4(4), pp. 499-502.

  8. Lukin, V.V., Zelensky, A.A., Tulyakova, N.O., Melnik, V.P. et al., (2000) Locally Adaptive Processing of 1-D Signals Using Z-parameter and Filter Banks, Proc. of NORSIG2000, Kolmarden, Sweden, pp. 195-198.

  9. Donoho, D.L. and Johnstone, I.M., (1995) Adapting to unknown smoothness by wavelet shrinkage, J. of American Statistical Association, 90(11), pp. 1200-1224.

  10. Abosekeen, A., Noureldin, A., and Korenberg, M.J., (2019) Improving the RISS/GNSS Land-Vehicles Integrated Navigation System Using Magnetic Azimuth Updates, Accepted to IEEE Transactions on Intelligent Transportation Systems, 14 p.

  11. Lukin, V.V., Fevralev, D.V., Abramov, S.K., Peltonen, S., and Astola, J., (2008) Adaptive DCT-based 1-D filtering of Poisson and mixed Poisson and impulsive noise, CD ROM Proceedings of LNLA, Switzerland, 8 p.

  12. Astola, J., Katkovnik, V., and Egiazarian, K., (2006) Local Approximation Techniques in Signal and Image Processing, SPIE Press Monograph, PM157.

  13. Abramov, S., Krivenko, S., Roenko, A., Lukin, V. et al., (2013) Prediction of filtering efficiency for DCT-based image denoising, Proc. of MECO 2013, Budva, Montenegro, pp. 97-100.

  14. Rubel, O., Abramov, S., Lukin, V., Egiazarian, K., Vozel, B., and Pogrebnyak, A., (2018) Is Texture Denoising Efficiency Predictable, International Journal on Pattern Recognition and Artificial Intelligence, 32, 1860005, 32 p.

  15. Rubel, O.S., Lukin, V.V., and de Medeiros, F.S., (2015) Prediction of Despeckling Efficiency of DCT-based filters Applied to SAR Images, Proceedings of DCOSS, Fortaleza, Brazil, pp. 159-168.

  16. Rubel, O. and Lukin, V., (2014) An Improved Prediction of DCT-Based Filters Efficiency Using Regression Analysis, Information and Telecommunication Sciences, 5(1), pp. 30-41, (in Ukrainian).

  17. Cameron, C., Windmeijer, A., Frank, A.G., Gramajo, H., Cane, D.E., and Khosla, C., (1997) An R-squared measure of goodness of fit for some common nonlinear regression models, J. of Econometrics, 77(2), 16 p.

  18. Oktem, R., Yarovslavsky, L., and Egiazarian, K. (1998) Signal and image denoising in transform domain and wavelet shrinkage: A comparative study, Proceedings of 9th European Signal Processing Conference, EUSIPCO.