Home   >   CSC-OpenAccess Library   >    Manuscript Information
Robustness of Median Filter For Suppression of Salt and Pepper Noise (SPN) and Random Valued Impulse Noise (RVIN)
Abdul Rasak Zubair, Hammed Oyebamiji Busari
Pages - 12 - 27     |    Revised - 31-01-2018     |    Published - 30-04-2018
Volume - 12   Issue - 1    |    Publication Date - April 2018  Table of Contents
Image Noise, Noise Density, Image Frequency, Median Filter, Peak Signal To Noise Ratio.
Noises in images are caused by many sources. Image de-noising has remained an active area of research. Results of numerical experiments on the robustness of median filter for suppression of Salt and Pepper Noise (SPN) and Random Valued Impulse Noise (RVIN) of varying noise densities are presented and discussed. Varying densities of SPN and RVIN were simulated and used to corrupt five selected test images which have different image frequencies. The corrupted images were filtered with Median Filters which has 3 by 3 kernel size. The effects of larger kernels were also examined. The performance metrics are the Peak Signal to Noise Ratio (PSNR) and Gain. SPN is found to have more adverse effects on images than RVIN. However, the Median filter is found to achieve a higher degree of noise suppression with SPN than RVIN. Effects of SPN and RVIN increase with an increase in noise density. Median filtering of SPN and RVIN corrupted images are found to be satisfactory with 3 by 3 kernel for noise densities up to the maximum of 60% and 40% noise densities respectively. Median filter Gain is found to increase with noise density up 40% and then reduce with further increase in noise density. To some extent, there is some correlation between Median filter gain and test image frequency. Using 5 by 5 kernel may improve noise suppression but the resulting filter image is blurred. 3 by 3 is the optimum kernel size.
1 Google Scholar 
2 BibSonomy 
3 ResearchGate 
4 Doc Player 
5 Scribd 
6 SlideShare 
A. Bovik. Handbook of image and video processing. New York: Academic, 2000.
A.B. Hamza and K. Hamid. "Image Denoising: A Nonlinear Robust Statistical Approach." IEEE Trans. Signal Processing, vol. 49, pp. 3045-3054, Dec. 2001.
A.B. Hamza, P. Luque, J. Martinez, and R. Roman. "Removing noise and preserving details with relaxed median filters." J. Math. Imag. Vision, vol. 11, no. 2, pp. 161-177, Oct. 1999.
A.K. Das. "Review of Image Denoising Techniques." International Journal of Emerging Technology and Advanced Engineering, vol. 4, no. 8, pp. 519-522, Aug. 2014.
A.K. Jain. Fundamentals of digital image processing. India: Prentice-Hall, 1989.
A.R. Zubair and O.A. Fakolujo. "Development of Statistics and Convolution as Tools for Image Noise." African Journal of Computing & ICT, vol 6, pp. 53-66, Dec. 2013.
A.R. Zubair, O.A. Fakolujo and P.K. Rajan. (2009, May). "Digital watermarking of still images with color digital watermarks." In IEEE EUROCON, 2009, pp. 1338-1345.
A.S. Ali, and M. Hong. (2007). "Robust Detection Technique for Removing Random-Valued Impulse Noise." IEEE International Symposium on Signal Processing and Information Technology, 2007, pp. 575-577.
B. Chanda and D.D. Majumer. Digital Image Processing and Analysis. India: Prentice-Hall, 2000.
C. Pinar. "Removal of Random-Valued Impulsive Noise from Corrupted Images." IEEE Transactions on Consumer Electronics, vol. 55, pp. 2097-2104, 2009.
C. Saxena and D. Kourav. "Noises and Image Denoising Techniques: A Brief Survey." International Journal of Emerging Technology and Advanced Engineering, vol. 4, no. 3, March 2014) pp. 878-885, Mar. 2014.
D. Sen and N. Tiwari, "Edge Preservation with Noise Reduction in Arthritis Image." International Journal for Scientific Research & Development, vol. 2, no. 12, pp. 371-375, 2015.
F.C. Tony and S. Jianhong. Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods. Philadelphia: SIAM, 2005.
G. Duncan and G. Beresford. "Median filter behavior with seismic data." Geophysical Prospecting, vol. 43, no. 3, pp. 329-345, Apr. 1995.
G. Gupta, "Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter." International Journal of Soft Computing and Engineering (IJSCE), vol. 1, no. 5, pp. 304-311, Nov. 2011.
G.R. Arce and J.L. Paredes, "Image Enhancement and Analysis with Weighted Medians." Nonlinear Image Processing (S. Mitra and G. Sicuranza, eds. London: Academic Press), pp. 27-67, 2000.
H. Ibrahim, N.S.P. Kong and T.F. Ng. "Simple adaptive median filter for the removal of impulse noise from highly corrupted images." IEEE Trans. on Consumer Electronics, vol. 54, no. 4, pp. 1920 - 1927, Nov. 2008.
K. Gupta and S.K. Gupta. "Image Denoising Techniques - A Review paper." International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 2, no. 4. pp. 6-9, Mar. 2013.
L. Hongqiao and W. Shengqian. "A New Image Denoising Method Using Wavelet Transform." International Forum on Information Technology and Applications, May 2009, pp. 111-114.
MathWorks (Matlab). "Documentation for MathWorks products." http://www.mathworks.com/access/helpdesk/help/helpdesk.shtml [Jan. 1, 2009].
N. Shelke and S. Sharma, "Comparative performance analysis of removal of impulse noise using different Methods." International Journal of Engineering and Computer Science, vol. 4, no. 4, pp. 11553-11557, April 2015.
P. Chen and L. Chih-Yuan. "An efficient edge-preserving algorithm for removal of salt-and-pepper noise." IEEE Signal Processing Letters, vol. 15, pp. 833-836, Dec. 2008.
P. Patidar, M. Gupta, S. Srivastava and A.K. Nagawat. "Image De-noising by Various Filters for Different Noise." International Journal of Computer Applications, November 2010, vol. 9, no. 4, pp. 45-50, Nov. 2010.
R. Yang, L. Yin, M. Gabbouj, J. Astola and Y. Neuvo. "Optimal weighted median filters under structural constraints." IEEE Trans. Signal Processing, vol. 43, pp. 591-604, Mar. 1995.
R.C. Gonzalez and R. E. Woods. Digital Image Processing. Massachusetts: Addison-Wesley, 2002.
S. Roy, N. Sinha and A.K. Sen. "A New Hybrid Image Denoising Method." International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 491-497, 2010.
S. Saudia, J. Varghese, K. Nallaperumal, S.P. Mathew, A.J. Robin and S. Kavitha, "Salt & pepper impulse detection and median based regularization using Adaptive Median Filter." IEEE Region 10 Annual International Conference (TENCON), 2008, pp. 1-6.
S.S.O. Choy, Y. Chan and W. Siu "An Improved Quantitative Measure of Image Restoration Quality." Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'96), 1996, vol. III, pp 1613-1616.
USC-SIPI Image Database. "Standard Test Images." http://sipi.usc.edu/database/index.html [Oct. 5, 2006].
V. Govindaraj and G. Sengottaiyan. "Survey of Image Denoising using Different Filters", International Journal of Science, Engineering and Technology Research (IJSETR), vol. 2, no. 2, pp. 344-351, Feb. 2013.
V. Jayaraj and D. Ebenezer. "Anew switching based median filtering scheme and algorithm for removal of high density salt and pepper noise in images." EURASIP Journal on Advances in Signal Processing vol. 2010, pp. 1-11, June 2010.
Y. Dong and S. Xu. "A new directional weighted median filter for removal of random- valued impulse noise." IEEE Signal Processing Letters, vol. 14, pp.193-196, Mar. 2007.
Y. Dong, R.H. Chan and S. Xu, "A detection statistic for random valued impulse noise." IEEE Trans. Image Process., vol.16, pp. 1112-1120, Apr. 2007.
Dr. Abdul Rasak Zubair
University of Ibadan, Ibadan, Oyo State - Nigeria
Mr. Hammed Oyebamiji Busari
Electrical/Electronic Engineering Department - Nigeria