[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

An efficient nonlinear approach for removing fixed-value impulse noise from grayscale images

  • Special Issue Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Removal of salt and pepper noise has been one of the most interesting researches in the field of image preprocessing tasks; it has two simultaneous stringent demands: the suppression of impulses and the preservation of fine details. To address this problem, a scheme based on nonlinear filters is proposed; it consists of the introduction of a redescending M-estimator within the modified nearest neighbor filter. In order to analyze all pixels in the neighborhood, as well as to reduce the magnitude of the existing impulses, a redescending M-estimator is used; the remaining pixels are processed by the modified nearest neighbor filter to obtain the best estimation of a noise-free pixel. The impulsive suppression is applied on the entire image by using a sliding window; the local information obtained by this one also allows to calculate the thresholds that characterize the influence function tested in the redescending M-estimator. To suppress high density fixed-value impulse noise in large-size grayscale images, the proposal is implemented on a heterogeneous CPU–GPU architecture. The noise reduction and the processing time of the proposed approach are evaluated by extensive simulations; its effectiveness is verified by quantitative and qualitative results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ahmed, F., Das, S.: Removal of high-density salt-and-pepper noise in images with an iterative adaptive fuzzy filter using alpha-trimmed mean. IEEE Trans. Fuzzy Syst. 22(5), 1352–1358 (2014)

    Article  Google Scholar 

  2. Ali, A., Qadir, M.F.: A modified m-estimator for the detection of outliers. Pak. J. Stat. Oper. Res. 1(1), 49–64 (2005)

    Article  Google Scholar 

  3. Andrews, D.F., Hampel, F.R.: Robust Estimates of Location: Survey and Advances. Princeton University Press, Princeton (2015)

    MATH  Google Scholar 

  4. Boncelet, C.: Image noise models. In: Bovik, A.C. (ed.) The Essential Guide to Image Processing, 2nd edn, pp. 143–167. Academic Press, Boston (2009)

    Chapter  Google Scholar 

  5. Chen, C.L.P., Liu, L., Chen, L., Tang, Y.Y., Zhou, Y.: Weighted couple sparse representation with classified regularization for impulse noise removal. IEEE Trans. Image Process. 24(11), 4014–4026 (2015)

    Article  MathSciNet  Google Scholar 

  6. Cheng, J., Grossman, M., McKercher, T.: Professional Cuda C Programming. Wiley, New York (2014)

    Google Scholar 

  7. Chou, H.-H., Hsu, L.-Y.: A noise-ranking switching filter for images with general fixed-value impulse noises. Sig. Process. 106, 198–208 (2015)

    Article  Google Scholar 

  8. Chou, H.-H., Hsu, L.-Y., Hwai-Tsu, H.: Turbulent-pso-based fuzzy image filter with no-reference measures for high-density impulse noise. IEEE Trans. Cybern. 43(1), 296–307 (2013)

    Article  Google Scholar 

  9. Eklund, A., Dufort, P., Forsberg, D., LaConte, S.M.: Medical image processing on the gpu—past, present and future. Med. Image Anal. 17(8), 1073–1094 (2013)

    Article  Google Scholar 

  10. Frigui, H., Krishnapuram, R.: A robust algorithm for automatic extraction of an unknown number of clusters from noisy data. Pattern Recogn. Lett. 17(12), 1223–1232 (1996)

    Article  MATH  Google Scholar 

  11. Gallegos-Funes, F.J., Ponomaryov, V.I.: Real-time image filtering scheme based on robust estimators in presence of impulsive noise. Real-Time Imaging 10(2), 69–80 (2004)

    Article  Google Scholar 

  12. Gupta, V., Chaurasia, V., Shandilya, M.: Random-valued impulse noise removal using adaptive dual threshold median filter. J. Vis. Commun. Image Represent. 26, 296–304 (2015)

    Article  Google Scholar 

  13. Habib, M., Hussain, A., Rasheed, S., Ali, M.: Adaptive fuzzy inference system based directional median filter for impulse noise removal. AEU-Int. J. Electron. Commun. 70(5), 689–697 (2016)

    Article  Google Scholar 

  14. Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Hampel, F.R., Ronchetti, E.M., Stahel, W.A.: Robust Statistics: The Approach Based on Influence Functions. Wiley, New York (1986)

    MATH  Google Scholar 

  15. Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A.: Robust Statistics: The Approach Based on Influence Functions, vol. 114. Wiley, New York (2011)

    MATH  Google Scholar 

  16. Hodges Jr., J.L., Lehmann, E.L.: Estimates of location based on rank tests. Ann. Math. Stat. 34, 598–611 (1963)

    Article  MathSciNet  MATH  Google Scholar 

  17. Hosseini, H., Marvasti, F.: Fast restoration of natural images corrupted by high-density impulse noise. EURASIP J. Image Video Process. 2013(1), 1–7 (2013)

    Article  Google Scholar 

  18. Hosseini, H., Hessar, F., Marvasti, F.: Real-time impulse noise suppression from images using an efficient weighted-average filtering. IEEE Signal Process. Lett. 22(8), 1050–1054 (2015)

    Article  Google Scholar 

  19. Huber, P.J.: The basic types of estimates. In: Huber, P.J. (ed.) Robust Statistics, pp. 43–72. Wiley, Hoboken (1981)

    Chapter  Google Scholar 

  20. Ibrahim, H., Neo, K.C., Teoh, S.H., Ng, T.F., Chieh, D.C.J., Hassan, N.F.N.: Impulse noise model and its variations. Int. J. Comput. Electr. Eng. 4(5), 647 (2012)

    Article  Google Scholar 

  21. Li, Y., Sun, J., Luo, H.: A neuro-fuzzy network based impulse noise filtering for gray scale images. Neurocomputing 127, 190–199 (2014)

    Article  Google Scholar 

  22. Li, Z., Cheng, Y., Tang, K., Yong, X., Zhang, D.: A salt and pepper noise filter based on local and global image information. Neurocomputing 159, 172–185 (2015)

    Article  Google Scholar 

  23. Malinski, L., Smolka, B.: Fast adaptive switching technique of impulsive noise removal in color images. J. Real-Time Image Process. (2016). https://doi.org/10.1007/s11554-016-0599-6

    Google Scholar 

  24. Maronna, R.A.R.D., Martin, D., Yohai, V.: Robust Statistics. Wiley, Chichester (2006)

    Book  MATH  Google Scholar 

  25. Mújica-Vargas, D., Gallegos-Funes, F.J., Rosales-Silva, A.J.: A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation. Pattern Recogn. Lett. 34(4), 400–413 (2013)

    Article  Google Scholar 

  26. Mújica-Vargas, D., Gallegos-Funes, F.J., Rosales-Silva, A.J., de Jesús Rubio, J.: Robust c-prototypes algorithms for color image segmentation. EURASIP J. Image Video Process. 2013(1), 1 (2013)

    Article  Google Scholar 

  27. Owens, J., Luebke, D.: Intro to parallel programming. http://www.nvidia.com/object/cuda_home_new.html/. [Online] Accessed 16 June 2016

  28. Pitas, I., Venetsanopoulos, A.N.: Median filters. In: Pitas, I., Venetsanopoulos, A.N. (eds.) Nonlinear Digital Filters, pp. 63–116. Springer, Berlin (1990)

    Chapter  Google Scholar 

  29. Pitas, I., Venetsanopoulos, A.N.: Order statistics in digital image processing. Proc. IEEE 80(12), 1893–1921 (1992)

    Article  Google Scholar 

  30. Poularikas, A.D.: Handbook of Formulas and Tables for Signal Processing. CRC Press, London (1998)

    Book  MATH  Google Scholar 

  31. Sánchez, M.G., Vidal, V., Bataller, J., Arnal, J.: A parallel method for impulsive image noise removal on hybrid CPU/GPU systems. Proc. Comput. Sci. 18, 2504–2507 (2013)

    Article  Google Scholar 

  32. Shevlyakov, G., Morgenthaler, S., Shurygin, A.: Redescending m-estimators. J. Stat. Plan. Inference 138(10), 2906–2917 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  33. Teoh, S.H., Ibrahim, H.: Variations on impulse noise model in digital image processing field: a survey on current research inclination. Int. J. Innov. Manag. Technol. 4(4), 393 (2013)

    Google Scholar 

  34. Tukey, J.W.: A survey of sampling from contaminated distributions. Contrib. Prob. Stat. 2, 448–485 (1960)

    MathSciNet  MATH  Google Scholar 

  35. Ullah, I., Qadir, M.F., Ali, A.: Insha’s redescending m-estimator for robust regression: a comparative study. Pak. J. Stat. Oper. Res. 2(2), 135–144 (2006)

    Article  Google Scholar 

  36. Vijaykumar, V.R., Vanathi, P.T., Kanagasabapathy, P., Ebenezer, D.: Robust statistics based algorithm to remove salt and pepper noise in images. Int. J. Inf. Commun. Eng. 5(3), 164–173 (2009)

    Google Scholar 

  37. Zhang, C., Wang, K.: A switching median-mean filter for removal of high-density impulse noise from digital images. Opt. Int. J. Light Electron Opt. 126(9), 956–961 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful with the editor and with the reviewers for their valuable comments and insightful suggestions, which can help to improve this research significantly. The authors thank to CONACYT as well as Tecnológico Nacional de México (TecNM)/Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET) for their financial support through the project 5688.16-P named "Sistema para procesamiento de imágenes de resonancia magnética para segmentación 3D y visualización de tejidos cerebrales".

Author information

Authors and Affiliations

  1. Department of Computer Science, CENIDET, Interior Internado Palmira s/n, Palmira, Cuernavaca-Morelos, Mexico

    Dante Mújica-Vargas

  2. Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional, Av. de las Granjas no. 682, Col. Santa Catarina, 02250, Ciudad de México, Mexico

    José de Jesús Rubio

  3. Department of Computational Systems, ITESHU, Domicilio Conocido s/n, El Saucillo, 42411, Huichapan, Hidalgo, Mexico

    Jean Marie Vianney Kinani

  4. Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Av. IPN s/n, Edificio Z, acceso 3, 3er piso; SEPI-Electrónica, Col. Lindavista, 07738, Ciudad de México, Mexico

    Francisco J. Gallegos-Funes

Authors
  1. Dante Mújica-Vargas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José de Jesús Rubio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean Marie Vianney Kinani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francisco J. Gallegos-Funes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dante Mújica-Vargas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mújica-Vargas, D., de Jesús Rubio, J., Kinani, J.M.V. et al. An efficient nonlinear approach for removing fixed-value impulse noise from grayscale images. J Real-Time Image Proc 14, 617–633 (2018). https://doi.org/10.1007/s11554-017-0746-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-017-0746-8

Keywords

Search

Navigation