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A study on genetic expression programming-based approach for impulse noise reduction in images

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Abstract

Existing impulse noise reduction techniques perform well at low noise densities; however, their performance drops sharply at higher noise densities. In this paper, we propose a two-stage scheme to surmount this problem. In the proposed approach, first stage consists of impulse detection unit followed by the filtering operation in the second stage. We have employed a genetic expression programming-based classifier for the detection of impulse noise-corrupted pixels. To reduce the blurring effect caused due to filtering operation on the noise-free pixels, we filter the detected noisy pixels only by using a modified median filter. Better peak signal-to-noise ratio, structural similarity index measure, and visual output imply the efficacy of the proposed scheme for noise reduction at higher noise densities.

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Notes

  1. ‘Etilism’ is the cloning of best chromosomes to next population or generation.

  2. For the simplicity of expressing the mathematical equations, we have used arbitrary variables for 2, 3, and 4 variable operators by \((x,y), (x,y,z)\), and \((a,b,c,d)\), respectively. We have considered a post-order traversing scheme to represent the actual variables (represented by leaves in the ET) by the arbitrary variables mentioned above.

References

  1. Luo, W.: An efficient detail-preserving approach for removing impulse noise in images. IEEE Signal Process. Lett. 13(7), 413–416 (2006)

    Article  Google Scholar 

  2. Bhadouria, V.S., Ghoshal, D., Siddiqi, A.H.: A new approach for high density saturated impulse noise removal using decision-based coupled window median filter. Signal Image Video Process. 8(1), 71–84 (2014)

  3. Esakkirajan, S., Veerakumar, T., Subramanyam, A., PremChand, C.: Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Process. Lett. 18(5), 287–290 (2011)

    Article  Google Scholar 

  4. Nair, M.S., Raju, G.: A new fuzzy-based decision algorithm for high-density impulse noise removal. Signal Image Video Process. 6(4), 579–595 (2012)

    Article  Google Scholar 

  5. Zhu, Z., Zhang, X., Wan, X., Wang, Q.: A random-valued impulse noise removal algorithm with local deviation index and edge-preserving regularization. Signal Image Video Process. pp 1–8 (2013). doi:10.1007/s11760-013-0426-5

  6. Wu, J., Tang, C.: Random-valued impulse noise removal using fuzzy weighted non-local means. Signal, Image Video Process. 8(2), 349–355 (2014)

    Article  Google Scholar 

  7. Chen, T., Wu, H.R.: Adaptive impulse detection using center-weighted median filters. IEEE Signal Process. Lett. 8(1), 1–3 (2001)

  8. Aizenberg, I., Butakoff, C.: Effective impulse detector based on rank-order criteria. IEEE Signal Process. Lett. 11(3), 363–366 (2004)

    Article  Google Scholar 

  9. Fischer, V., Lukac, R., Martin, K.: Cost-effective video filtering solution for real-time vision systems. EURASIP J. Appl. Signal Process. 13, 2026–2042 (2005)

    Article  Google Scholar 

  10. Lien, C.Y., Huang, C.C., Chen, P.Y., Lin, Y.F.: An efficient denoising architecture for removal of impulse noise in images. IEEE Trans. Comput. 62(4), 631–643 (2013)

    Article  MathSciNet  Google Scholar 

  11. Xiong, B., Yin, Z.: A universal denoising framework with a new impulse detector and nonlocal means. IEEE Trans. Image Process. 21(4), 1663–1675 (2012)

    Article  MathSciNet  Google Scholar 

  12. Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst. 13(2), 87–129 (2001)

    MATH  Google Scholar 

  13. Zhou, C., Xiao, W., Tirpak, T., Nelson, P.: Evolving accurate and compact classification rules with gene expression programming. IEEE Trans. Evol. Comput. 7(6), 519–531 (2003)

    Article  Google Scholar 

  14. Sermpinis, G., Laws, J., Karathanasopoulos, A., Dunis, C.L.: Forecasting and trading the EUR/USD exchange rate with gene expression and psi sigma neural networks. Expert Syst. Appl. 39(10), 8865–8877 (2012)

    Article  Google Scholar 

  15. Fawcett, T.: ROC graphs: notes and practical considerations for researchers. Technical. report, HP Laboratories, Palo Alto, USA (2004)

  16. Forouzan, A., Araabi, B.: Iterative median filtering for restoration of images with impulsive noise. In: Proceedings of the 10th IEEE International Conference on Electronics, Circuits and Systems, vol 1, 232–235 (2003)

  17. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge. MIT Press, Cambridge (1992)

    Google Scholar 

  18. Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence). Springer, New York (2006)

    Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Agartala, 799055, Tripura (West), India

    Vivek Singh Bhadouria & Dibyendu Ghoshal

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  1. Vivek Singh Bhadouria
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  2. Dibyendu Ghoshal
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Correspondence to Vivek Singh Bhadouria.

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Bhadouria, V.S., Ghoshal, D. A study on genetic expression programming-based approach for impulse noise reduction in images. SIViP 10, 575–584 (2016). https://doi.org/10.1007/s11760-015-0780-6

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  • DOI: https://doi.org/10.1007/s11760-015-0780-6

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