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An interpolation filter based on natural neighbor Galerkin method for salt and pepper noise restoration with adaptive size local filtering window

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Abstract

In this paper, we present a new interpolation filter for salt and pepper noise (SPN) restoration in digital images. The proposed filter is established on decision-based filters (DBF) and consists of two units: noise detection and noise restoration. In noise restoration unit, the natural neighbor Galerkin method (NNGM), which is a two-dimensional scattered data interpolation, is adopted for estimating the intensities of noisy pixels. For each detected noisy pixel, an adaptive size local filtering window is considered and NNGM interpolation is applied locally. Compared to the other state-of-the-art DBFs for SPN removal, which are based on interpolation methods, our proposed method has better performance in terms of both objective and subjective assessments. In our proposed method, the noise restoration unit requires no manual parameter tuning. Numerous experiments results demonstrate that our proposed filter removes SPN effectively and preserves the details and edges well.

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References

  1. Bovik, A.C.: Handbook of image and video processing. Academic press, New York (2000)

    MATH  Google Scholar 

  2. Chatterjee, P., Milanfar, P.: Is denoising dead? IEEE Trans. Image Process. 19(4), 895–911 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  3. Sree, P.S.J., Kumar, P., Siddavatam, R., Verma, R.: Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets. Signal Image Video Process. 7(1), 111–118 (2013)

    Article  Google Scholar 

  4. Vasanth, K., Kumar, V.J.S.: Decision-based neighborhood referred unsymmetrical trimmed variants filter for the removal of high-density salt-and-pepper noise in images and videos. Signal Image Video Process. 9(8), 1833–1841 (2015)

    Article  Google Scholar 

  5. Lamichhane, B.P.: Finite element techniques for removing the mixture of Gaussian and impulse noise. IEEE Trans. Signal Process. 57(7), 2538–2547 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Jayasree, P.S., Raj, P., Kumar, P., Siddavatam, R., Ghrera, S.P.: A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines. Signal Image Video Process. 7(6), 1145–1157 (2013)

    Article  Google Scholar 

  7. Sanaee, P., Moallem, P., Razzazi, F.: Structured-based interpolation method for restoring the intensity of low-density impulse noise. IET Image Process. 12(9), 1577–1585 (2018)

    Article  Google Scholar 

  8. Kalyoncu, C., Toygar, Ö., Demirel, H.: Interpolation-based impulse noise removal. IET Image Process. 7(8), 777–785 (2013)

    Article  Google Scholar 

  9. Bai, T., Tan, J., Hu, M., Wang, Y.: A novel algorithm for removal of salt and pepper noise using continued fractions interpolation. Signal Process. 102, 247–255 (2014)

    Article  Google Scholar 

  10. Veerakumar, T., Esakkirajan, S., Vennila, I.: Recursive cubic spline interpolation filter approach for the removal of high density salt-and-pepper noise. Signal Image Video Process. 8(1), 159–168 (2014)

    Article  Google Scholar 

  11. Zhang, P., Li, F.: A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE Signal Process. Lett. 21(10), 1280–1283 (2014)

    Article  Google Scholar 

  12. Li, Z., Liu, G., Xu, Y., Cheng, Y.: Modified directional weighted filter for removal of salt and pepper noise. Pattern Recognit. Lett. 40, 113–120 (2014)

    Article  Google Scholar 

  13. Chou, H.H., Hsu, L.Y., Hu, H.T.: Multi-level adaptive switching filters for highly corrupted images. J. Vis. Commun. Image Represent. 30, 363–375 (2015)

    Article  Google Scholar 

  14. Kandemir, C., Kalyoncu, C., Toygar, Ö.: A weighted mean filter with spatial-bias elimination for impulse noise removal. Digit. Signal Process. 46, 164–174 (2015)

    Article  MathSciNet  Google Scholar 

  15. Veerakumar, T., Jagannath, R.P., Subudhi, B.N., Esakkirajan, S.: Impulse noise removal using adaptive radial basis function interpolation. Circuits Syst. Signal Process. 36(3), 1192–1223 (2017)

    Article  Google Scholar 

  16. Belikov, V.V., Ivankov, V.D., Kontorovich, V.K., Korytnik, S.A., Semenov, Y.A.: The non-Sibsonian interpolation: a new method of interpolation of the values of a function on arbitrary set of points. Comput. Math. Math. Phys. 37(1), 9–15 (1997)

    MathSciNet  MATH  Google Scholar 

  17. Sukumar, N., Moran, B., Semenov, A.Y., Belikov, V.V.: Natural neighbor Galerkin methods. Int. J. Numer. Methods Eng. 50(1), 127 (2001)

    Article  MATH  Google Scholar 

  18. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. Xue, W., Zhang, L., Mou, X., Bovik, A.C.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. 23(2), 684–695 (2014)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Payman Moallem.

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Sanaee, P., Moallem, P. & Razzazi, F. An interpolation filter based on natural neighbor Galerkin method for salt and pepper noise restoration with adaptive size local filtering window. SIViP 13, 895–903 (2019). https://doi.org/10.1007/s11760-019-01426-3

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  • DOI: https://doi.org/10.1007/s11760-019-01426-3

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