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A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines

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Abstract

This paper discusses a novel algorithm for salt and pepper image noise cancelation using cardinal B-splines. The purpose of this paper is to present an analysis and application of cardinal B-splines for image noise cancellation. To apply the cardinal B-splines, one should analyze the different properties of the cardinal B-spline. Here we make use of the interpolation property and compact support of the cardinal B-splines. There are various assumptions and conditions that are considered while applying the cardinal B-splines for noise removal. The result of denoising the images affected up to 95 % of the salt and pepper noise has been shown. The results of proposed method are being compared with the other existing methods, and the comparison shows the better performance of our method.

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References

  1. Michael U.: Splines: a perfect fit for signal and image processing. IEEE Signal Process. Mag. 16(6), 24–38 (1999)

    Google Scholar 

  2. Jaiswal, T., Siddavatam, R.: Image noise cancellation by lifting filter using second generation wavelets. In: Proceedings of IEEE International Conference on Advances in Recent Technologies in Communication and Computing, pp. 667–671. Kerala, India, 27–28 Oct. (2009)

  3. Siddavatam, R., Sandeep, K., Mittal, R.K.: A fast progressive image sampling using lifting scheme and non-uniform B-Splines. In: Proceedings of IEEE International Symposium on Industrial Electronics ISIE-07, pp. 1645–1650. Vigo, Spain, 4–7 June (2007)

  4. Sun T., Neuvo Y.: Detail-preserving median based filters in image processing. Pattern Recogn. Lett. 15, 341–347 (1994)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Raymond H.C., Chung-Wa H., Mila N.: Salt and peeper noise removal by median-type noise detectors and detail-preserving regularization. In: IEEE Trans. Image Process. 14(10), 1479–1485 (2005)

    Article  Google Scholar 

  7. Pok G., Liu J.C., Nair A.S.: Selective removal of impulse noise based on homogeneity level information. In: IEEE Trans. Image Process. 12(1), 85–92 (2003)

    Article  Google Scholar 

  8. Chen T., Wu H.R.: Application of partition-based median type filters for suppressing noise in images. In: IEEE Trans. Image Process. 10(6), 829–836 (2001)

    Article  MATH  Google Scholar 

  9. Chan R.H., Hu C., Nikolova M.: An iterative procedure for removing random-valued impulse noise. In: IEEE Signal Process. Lett. 11(12), 921–924 (2004)

    Article  Google Scholar 

  10. Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P.: Image quality assessment: from error visibility to structural similarity. In: IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  11. Swelden W.: The lifting scheme: a construction of second generation wavelets. SIAM J. Math. Anal. 29(2), 511–546 (1998)

    Article  MathSciNet  Google Scholar 

  12. Lee J.S.: Digital image enhancement and filtering by use of local statistics. In: IEEE Trans. Pattern Anal. Mach. Intell. PAMI 2, 165–168 (1980)

    Article  Google Scholar 

  13. Kuan D.T., Sawchuk A.A., Strand T.C., Chavel P.: Adaptive noise smoothing filter for images with signal-dependent noise. In: IEEE Trans. Pattern Anal. Mach. Intell. PAMI 7(2), 165–177 (1985)

    Article  Google Scholar 

  14. Bernstein R.: Adaptive nonlinear filters for simultaneous removal of different kinds of noise in images. In: IEEE Trans. Circ. Syst. CAS 34(11), 1275–1291 (1987)

    Article  Google Scholar 

  15. Sun X.Z., Venetsanopoulos A.N.: Adaptive schemes for noise filtering and edge detection by use of local statistics. In: IEEE Trans. Circ. Syst. CAS 35(1), 57–69 (1988)

    Article  Google Scholar 

  16. Fahmy M.F., Gamal Fahmy, Fahmy O.F.: B-spline wavelets for signal denoising and image compression. Springer J. Signal Image Video Process. 5(2), 141–153 (2009)

    Article  Google Scholar 

  17. Jayasree P.S., Kumar P., Siddavatam R., Ravi kant V.: Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets. Springer J. Signal Image Video Process. 5(2), 1–8 (2011)

    Google Scholar 

  18. Ognyan K.: Multivariate Polysplines: Applications to Numerical and Wavelet Analysis. Academic Press, London (2001)

    Google Scholar 

  19. Biswas S., Lovell B.C.: Bézier and Splines in Image Processing and Machine Vision. Springer, London (2008)

    Book  MATH  Google Scholar 

  20. Bartels R.H., Beatty J.C., Barsky B.A.: An Introduction to Splines for Use in Computer Graphics and Geometry Modelling. Morgan Kaufmann, Los Altos (1987)

    Google Scholar 

  21. Arce G.R., McLoughlin M.P.: Theoretical analysis of the MAX/Median filter. In: IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 60–69 (1987)

    Article  Google Scholar 

  22. Gonzalez R.C., Woods R.E.: Digital Image Processing. Prentice Hall, Englewood Cliffs (2006)

    Google Scholar 

  23. Michael U., Akram A., Murray E.: Fast B-Spline transforms for continuous image representation and interpolation. In: IEEE Trans. Pattern Anal. Mach. Intell. 13(3), 277–285 (1991)

    Article  Google Scholar 

  24. Erik M., Michael U.: A note on cubic convolution interpolation. In: IEEE Trans. Image Process. 12(4), 477–479 (2003)

    Article  Google Scholar 

  25. Zhou W., David Z.: Progressive switching median filter for the removal of impulse noise from highly corrupted images. In: IEEE Trans. Circ. Syst. II Analog Digital Signal Process. 46(1), 78–80 (1999)

    Article  Google Scholar 

  26. Prasad L., Iyengar S.S.: Wavelet Analysis with Application to Image Processing. CRC Press, Boca Raton (1997)

    Google Scholar 

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Correspondence to Rajesh Siddavatam.

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Jayasree, P.S., Raj, P., Kumar, P. et al. A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines. SIViP 7, 1145–1157 (2013). https://doi.org/10.1007/s11760-012-0368-3

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  • DOI: https://doi.org/10.1007/s11760-012-0368-3

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