Skip to main content
SpringerLink
Log in

A Decision Based Neighbourhood Referred Asymmetrically Trimmed Modified Trimean for the Removal of High Density Salt and Pepper Noise in Images and Videos

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

A Correction to this article was published on 19 May 2021

This article has been updated

Abstract

A Decision Based Neighbourhood Referred Asymmetrically Trimmed Modified Trimean for the Removal of High Density salt and pepper noise in Images and videos is proposed. The proposed algorithm initially checks for the outliers in a 3 × 3 neighbourhood. If the processed pixel is noisy then check for the presence of noisy pixels with the 4 neighbours; If the 4 neighbours are found to hold outliers then mean of the 4 neighbours will replace the output. If the 4 neighbours are not noisy then the output is replaced by asymmetrically trimmed Modified Trimean. If all the pixels of the current processing window are noisy then the mean of all elements will replace the processed pixel. If the processed pixel does not hold the outlier then the pixel is termed as not noisy and left unaltered. The proposed algorithm exhibit excellent noise elimination capability with enhanced edge preservation capability. The algorithm was tested on a standard database and the results of the proposed algorithm were compared to 16standard and existing algorithms. The proposed algorithm exhibit excellent results in terms of both Quantitative and qualitative measures.

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

Access this article

Log in via an institution

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Change history

References

  1. J. Astola and P. Kusmanen . Fundamentals of non linear digital filtering, CRC press; 1997

  2. Hwang, H., & Haddad, R. A. (1995). Adaptive median filters: New algorithms and results. IEEE transaction on Image processing, 4, 499–502

    Article  Google Scholar 

  3. Brownrigg, D. R. K. (1984). “The weighted median filter”, commun. ACM, vol. 27, no. 8, pp. 807–818.

  4. Ko, S. J., & Lee, Y. H. (1991). Center weighted median filters and their application to image enhancement. IEEE Transactions on Circuits Systems, 38(9), 984–993

    Article  Google Scholar 

  5. Tao Chen ; Kai-Kuang Ma ; Li-Hui Chen, “Tri-state median filter for image denoising”, IEEE Transactions on Image Processing,Vol 8, Issue 12, Dec 1999.

  6. Z.Wang and D.Zhang (1999), “Progressive switching median filter for removal of impulse noise from highly corrupted images”, IEEE Transactions on Circuits Systems-II, no.46, pp78–80.

  7. Chan, R. H., Ho, C.-W., & Nikolova, M. (2009). Salt and pepper noise removal by median – type noise detectors and detail preserving regularization. IEEE transactions on image processing, 14–10, 1479–1485

    Google Scholar 

  8. K.S.Srinivasan And D.Ebenezer (2007) A New Fast and Efficient Decision-Based Algorithm for removal of high-density Impulse noises. IEEE Signal Processing letters, 14(3): 189–192.

  9. Madhu S., Nair, K.Revathy, Rao. Tatavarti., (2008), “An Improved Decision Based Algorithm for impulse noise removal”, Proceedings in Congress on Image and Signal Processing, Pages 426–431.

  10. Tena J, K.Vasanth, Govindaswamy Indhumathi, (2014) “An Enhanced Decision based Algorithm for the Reduction of High Density Salt and Pepper Noise with Reduced Streaks” Proceedings in International Conference on Electronics and Communication Systems, Feb.13 –14, 2014.

  11. Aiswarya K., Jayaraj V., Ebenezer D., (2010), "A New and efficient Algorithm for the removal of high density salt & pepper noise in images & videos, Proceedings in International conference on computer modelling and simulation, Pages 409–413.

  12. Esakkirajan, S., Veerakumar, T., Subramanyam, A. N., & Prem Chand, C. H. (2011). Removal of high density Salt and pepper noise through modified decision based Unsymmetrical trimmed median filter. IEEE Signal processing letters, 18(5), 287–290

    Article  Google Scholar 

  13. T. Veerakumar, S. Esakkirajan, Ila Vennila, (2012) ” An Approach to Minimize Very High Density Salt and Pepper Noise through Trimmed Global Mean”, International Journal of Computer Applications , Vol 39– No.12.

  14. Vasanth, K., & Jawahar Senthil kumar, V. (2012). “A decision based unsymmetrical trimmed mid point algorithm for the removal of high density salt and pepper noise. Journal of Applied Theoretical and Information Technology, 42(2), 553–563

    Google Scholar 

  15. K. Vasanth , K. Kumar , S. Saravanan, “Decision based unsymmetrical trimmed mode filter for the removal of salt and pepper noise in images”, proceedings on International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp 1–7,2015

  16. Vasanth, K., & Jawahar Senthil Kumar, V. (2014). A decision based neighborhood referred unsymmetrical trimmed variants filter for the removal of salt and pepper noise in images and videos”. International Journal of Signal, vision and video processing, Springer, august, 9(8), 1833–1841

    Google Scholar 

  17. Balasubramanian, S., Kalishwaran, S., Muthuraj, R., Ebenezer, D., Jayaraj, V., (2009), “An efficient Non linear cascade filtering algorithm for removal of high density salt and pepper noise in image and video sequence”, Proceedings in International Conference on control, Automation, communication and Energy Conservation, Pages 1–6

  18. . K.Vasanth, Tena J, Elanangai.V, Amuthan, “ Cascaded Algorithm for the removal of impulse noise variants and artifacts in images”, International journal of Applied Engineering Research, Volume 9, Number 19 (2014) pp. 5779–5796, 2014.

  19. Syamala Jayasree, P., Raj, P., Kumar, P., Rajesh Siddavatam, S. P., & Ghrera, . (2012). A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines. International journal of Signal image and video processing, Springer,. https://doi.org/10.1007/s11760-012-0368-3

    Article  Google Scholar 

  20. Bai, T., Tan, J., Min, Hu., & Yan wang, . (2014). A novel algorithm for removal of salt and pepper noise using continued fractions interpolation. Signal Processing, 102, 247–255

    Article  Google Scholar 

  21. Kishorebabu, V., Packyanathan, G., Kamatham, H., et al. (2017). J Image Video Proc., 2017, 67. https://doi.org/10.1186/s13640-017-0215-0

    Article  Google Scholar 

  22. Vasanth K, Varatharajan, Gunasekaran Manogaran, Priyan, Xio-Zhi,Gao, (2017) An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images https://doi.org/10.1016/j.compeleceng.2017.05.035

  23. Peixuan Zhang and Fang Li. (2014). A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE Signal Processing Letters, 21(10), 1280–1283

    Article  Google Scholar 

  24. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error measurement to structural similarity. IEEE Transactions on Image Processing, 13(4), 102–109

    Article  Google Scholar 

  25. Abdou, I. A., & Pratt, W. (1979). Quantitative design and evaluation of enhancement/thresholding edge detectors. Proceedings of the IEEE, 67(5), 753–766

    Article  Google Scholar 

  26. Erkan, U., Thanh, D. N., Enginoğlu, S., & Memiş, S. (2020, June). Improved Adaptive Weighted Mean Filter for Salt-and-Pepper Noise Removal. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (pp. 1-5). IEEE.

  27. http://sipi.usc.edu/database/ accessed on 25 March 2021

  28. Erkan, U., Enginoðlu, S., Thanh, D. N. H., & Hieu, L. M. (2020). Adaptive frequency median filter for the salt-and-pepper denoising problem. IET Image Processing, 14(7), 1291–1302

    Article  Google Scholar 

  29. Enginoglu, S., Erkan, U. & Memis, S. Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal. Multimed Tools Appl 78, (2019).

  30. Erkan, U., & Gokrem, L. (2018). A new method based on pixel density in salt and pepper noise removal. Turk J Elec Eng & Comp Sci, 26, 162–171

    Article  Google Scholar 

  31. Erkan, U., Gokrem, L., & Enginolu, S. (2018). Different applied median filter in salt and pepper noise. Computers & Electrical Engineering, 70, 789–798

    Article  Google Scholar 

  32. Christo, M. S., Vasanth, K., & Varatharajan, R. (2020). A decision based asymmetrically trimmed modified winsorized median filter for the removal of salt and pepper noise in images and videos. Multimed Tools Appl, 79, 415–432

    Article  Google Scholar 

  33. Vasanth, K., Varatharajan, R. A decision based unsymmetrical trimmed modified winsorized variants for the removal of high density salt and pepper noise in images and videos. Computer communications,154(1), 433–441,2020

  34. Zhou Wang and A. C. Bovik, "A universal image quality index," in IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81–84, March 2002,

Download references

Author information

Authors and Affiliations

  1. Department of E.C.E, Vidya Jyothi Institute of Technology, Aziz Nagar, Hyderabad, 500075, Telangana, India

    K Vasanth

Authors
  1. K Vasanth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K Vasanth.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original version of this article has been revised: Reference no. 26 has been corrected.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vasanth, K. A Decision Based Neighbourhood Referred Asymmetrically Trimmed Modified Trimean for the Removal of High Density Salt and Pepper Noise in Images and Videos. Wireless Pers Commun 120, 2585–2609 (2021). https://doi.org/10.1007/s11277-021-08547-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08547-4

Keywords

Search

Navigation