Improved LPA-ICI-based estimators embedded in a signal denoising virtual instrument

Signal, Image and Video Processing volume 11, pages211–218(2017)Cite this article

Abstract

Adaptive filters embedded in a user-friendly virtual instrument (VI) for signal denoising are proposed in the paper. The filters are based on the local polynomial approximation (LPA) and equipped with the improved intersection of confidence intervals (ICI) rule, known as the relative intersection of confidence intervals (RICI) rule, in order to achieve adaptivity to high transitions in signals and to vary the estimator size. The VI is also equipped with multiwindow estimation which significantly improves denoising quality, while preserving edges, instantaneous slope changes, and spikes in the signal. To reduce computational time, the fast ICI and fast RICI methods are provided. The LPA-RICI method is shown to outperform other adaptive denoising methods presented in the paper, reducing the estimation mean absolute error by up to 51.6 %, and the mean squared error by up to 81 %, while enhancing the execution time by up to 2 % when compared to the original LPA-ICI method.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Katkovnik, V.: A new method for varying adaptive bandwidth selection. IEEE Trans. Signal Process. 47(9), 2567–2571 (1999)

    Article  Google Scholar 

  2. 2.

    Katkovnik, V., Egiazarian, K., Astola, J.: Local approximation techniques in signal and image processing. SPIE Press, Bellingham (2006)

  3. 3.

    Katkovnik, V.: Multiresolution local polynomial regression: a new approach to pointwise spatial adaptation. Digit. Signal Process. 15(1), 73–116 (2005)

    Article  Google Scholar 

  4. 4.

    Katkovnik, V., Egiazarian, K., Astola, J.: Adaptive window size image de-noising based on intersection of confidence intervals (ICI) rule. J. Math. Imaging Vis. 16, 223–235 (2002)

    MathSciNet  Article  MATH  Google Scholar 

  5. 5.

    Lerga, J., Sucic, V.: Nonlinear IF estimation based on the pseudo WVD adapted using the improved sliding pairwise ICI rule. IEEE Signal Process. Lett. 16(11), 953–956 (2009)

    Article  Google Scholar 

  6. 6.

    Stankovic, L., Katkovnik, V.: Algorithm for the instantaneous frequency estimation using time-frequency distributions with adaptive window width. IEEE Signal Process. Lett. 5(9), 224–227 (1998). doi:10.1109/97.712105

    Article  Google Scholar 

  7. 7.

    Lerga, J., Sucic, V., Boashash, B.: An improved method for nonstationary signals components extraction based on the ICI rule. In: 7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011, pp. 307–310 (2011)

  8. 8.

    Gershman, A.B., Stanković, L., Katkovnik, V.: Sensor array signal tracking using a data-driven window approach. Signal Process. 80(12), 2507–2515 (2000)

    Article  MATH  Google Scholar 

  9. 9.

    Lerga, J., Vrankic, M., Sucic, V.: A signal denoising method based on the improved ICI rule. IEEE Signal Process. Lett. 15(8), 601–604 (2008)

    Article  MATH  Google Scholar 

  10. 10.

    Goldenshluger, A., Nemirovski, A.: On spatially adaptive estimation of nonparametric regression. Math. Methods Stat. 6(2), 1–35 (1997)

    MathSciNet  MATH  Google Scholar 

  11. 11.

    Sucic, V., Lerga, J., Vrankic, M.: Adaptive filter support selection for signal denoising based on the improved ICI rule. Digit. Signal Process. 23(1), 65–74 (2012)

    MathSciNet  Article  Google Scholar 

  12. 12.

    Lerga, J., Grbac, E., Sucic, V.: An ICI based algorithm for fast denoising of video signals. Automatika 55(3), 351–358 (2014)

    Google Scholar 

  13. 13.

    Donoho, D.L., Johnstone, J.M.: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90(432), 1200–1224 (1995)

    MathSciNet  Article  MATH  Google Scholar 

Download references

Author information

Affiliations

  1. Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000, Rijeka, Croatia

    Gordan Segon, Jonatan Lerga & Victor Sucic

Authors
  1. Gordan Segon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonatan Lerga
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Victor Sucic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonatan Lerga.

Electronic supplementary material

Below is the link to the electronic supplementary material.

A graphical user interface of the virtual instrument. (doc 6.49MB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Segon, G., Lerga, J. & Sucic, V. Improved LPA-ICI-based estimators embedded in a signal denoising virtual instrument. SIViP 11, 211–218 (2017). https://doi.org/10.1007/s11760-016-0921-6

Download citation

Keywords

  • Statistical signal processing
  • Signal filtering
  • Adaptive estimators
  • Intersection of confidence intervals (ICI)
  • Local polynomial approximation (LPA)