Unsymmetrical Trimmed Midpoint as Detector for Salt and Pepper Noise Removal

  • K. Vasanth
  • V. Jawahar Senthil Kumar
  • V. Elanangai
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


A fixed 3x3 window, Unsymmetrical trimmed midpoint is used as a detector for the detection of fixed valued impulse noise is proposed for the increasing noise densities. The processed pixel is termed as noisy, if the absolute difference between processed pixels and unsymmetrical trimmed midpoint is greater than fixed threshold. Under high noise densities the processed pixel is noisy, so the median of the ordered array is found. The median is checked using the above procedure. If found true then the computed median is considered as noisy hence the corrupted pixel is replaced by the Unsymmetrical Trimmed midpoint of the current processing window. If median is not noisy then replace the median of the current processing window else if the pixel is termed uncorrupted, it is left unaltered. The proposed algorithm (PA) is tested on different varying detail images. The proposed algorithm is compared with the standard algorithms and found to give good results both qualitative and quantitatively for increasing noise densities. The proposed algorithm eliminates salt and pepper noise up to 80% and preserves edges up to 70%.


Unsymmetrical trimmed midpoint filter salt and pepper noise threshold based midpoint filters 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pitas, I., Venetasanopoulos, A.N.: Non Linear Digital Filters: Principles and Applications. Kluwer, Boston (1990)Google Scholar
  2. 2.
    Astola, J., Kuosmanen, P.: Fundamentals of non linear digital filtering. CRC Press (1997)Google Scholar
  3. 3.
    Hwang, I.H., Hadded, R.A.: Adaptive Median filter: New algorithms and results. IEEE Transaction on image Processing 4(4), 499–502Google Scholar
  4. 4.
    Zhang, H.: Generalized Threshold Decomposition. Journal of Electronics 14(1) (January 1997)Google Scholar
  5. 5.
    Ko, S.-J., Lee, Y.-H.: Center weighted median filters and their applications to image enhancement. IEEE Trans. Circuits Syst. 38, 984–993 (1991)CrossRefGoogle Scholar
  6. 6.
    Ng, P.E., Ma, K.K.: A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Transactions on Image Processing 15(6), 1506–1516 (2006)CrossRefGoogle Scholar
  7. 7.
    Zhang, S., Karim, M.A.: A new impulse detector for switching median filters. IEEE Signal Processing Letters 9(11), 360–363 (2002)CrossRefGoogle Scholar
  8. 8.
    Naif Alajlan, K., Kamel, M., Jernigan, E.: Detail Preserving impulse noise removal. International Journal on Signal Processing: Image Communication 19, 993–1003 (2004)CrossRefGoogle Scholar
  9. 9.
    Bovik, A.: Handbook of Image and Video Processing. Academic Press (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • K. Vasanth
    • 1
  • V. Jawahar Senthil Kumar
    • 2
  • V. Elanangai
    • 1
  1. 1.Sathyabama UniversityChennaiIndia
  2. 2.Anna UniversityChennaiIndia

Personalised recommendations