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Removal of High Density Impulse Noise using Efficient Median Filter for Digital Image

by Trapti Soni, Narendra Rathor
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 5
Year of Publication: 2015
Authors: Trapti Soni, Narendra Rathor
10.5120/20148-2280

Trapti Soni, Narendra Rathor . Removal of High Density Impulse Noise using Efficient Median Filter for Digital Image. International Journal of Computer Applications. 115, 5 ( April 2015), 25-31. DOI=10.5120/20148-2280

@article{ 10.5120/20148-2280,
author = { Trapti Soni, Narendra Rathor },
title = { Removal of High Density Impulse Noise using Efficient Median Filter for Digital Image },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 5 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number5/20148-2280/ },
doi = { 10.5120/20148-2280 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:53:57.036159+05:30
%A Trapti Soni
%A Narendra Rathor
%T Removal of High Density Impulse Noise using Efficient Median Filter for Digital Image
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 5
%P 25-31
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An Efficient Median Filter (EMF) algorithm for removal or enhancement of gray scale images are highly corrupted impulse noise is proposed in this paper. Noise in image are represent the pixel value 0's and 255's are ensures that black and white dot in image. In proposed algorithm take an image and select 3x3 size window and target or center pixel value check if its value is 0's or 255's then image is corrupted otherwise noise free image. If image is noisy and target pixels neighboring pixel value is between 0's and 255's then we replace pixel value with the median value and if target pixels neighboring pixel value is 0's or 255's then we replace pixel value with the mean value. Else increased the window size and again repeat this process until image is denoised. The proposed filter algorithm shows better simulation result as compare the existing algorithms. The simulation result shows better and efficient performance of PSNR and MSE and computation time.

References
  1. Castleman Kenneth R, Digital Image Processing, Prentice Hall, NewJersey, 1979.
  2. Reginald L. Lagendijk, Jan Biemond, Iterative Identification and Restoration of Images, Kulwer Academic, Boston, 1991.
  3. Scott E Umbaugh, Computer Vision and Image Processing, Prentice HallPTR, New Jersey, 1998.
  4. Langis Gagnon," Wavelet Filtering of Speckle Noise-Some Numerical Results," Proceedings of the Conference Vision Interface 1999, TroisRiveres.
  5. A. K. Jain, Fundamentals of digital image processing. Prentice-Hall, 1989
  6. Z. Wang and D. Zhang, "Progressive switching median filter for the removal of impulse noise from highly corrupted images," IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 1999, vol. 46, no. 1, pp. 78-80.
  7. T. Chen and H. R. Wu, "Adaptive impulse detection using center weighted median filters," IEEE Signal Process. Lett. , vol. 8, no. 1, pp. 1–3, Jan. 2001
  8. S. -J. KO and Y. -H. Lee, "Centre weighted median filters and their applications to image enhancement," IEEE Trans. Circuits Syst. , vol. 38, no. 9,pp. 984- 993,Sept. 1991
  9. Review of Impulse Noise Reduction Techniques Manohar Annappa Koli Research Scholar, Department of Computer Science Tumkur university. 1/fnoise,"BrownianNoise,"http://classes. yale. edu/9900/math190a/OneOverF. html, 1999.
  10. Ko S. J. and Lee Y. H. , "Center Weighted Median Filters and their Applications to Image Enhancement," IEEE Trans. Circuits Systems, 38, No. 9, pp. 984 - 993, 1991.
  11. Arce G. and Paredes J. , "Recursive Weighted Median Filters Admitting Negative Weights and Their Optimization", IEEE Trans. on Signal Processing, 48, No. 3, pp. 768-779, 2000
  12. R. H. Chan, Chung-Wa Ho, M. Nikolova, "Salt and Pepper Noise Removal by Median Type Noise Detectors and Detail-Preserving Regularization", IEEE Transactions on Image Processing, 14 No. 10, pp. 1479-1485, October 2005
  13. Hani M. Ibrahem, "An Efficient and simple switching filter for removal of high density salt and pepper noise" I. J. Image ,Graphics and signal processing, published online October 2013,12,1-8 in MECS.
  14. Tao Chen, Kai-Kuang Ma, Li-Hui Chen "TriSstate Median Filter for Image Denoising" IEEE Transactions on Image Processing, Vol. 8, No. 12, December 1999, pp 1834-1838
  15. Jacques LévyVéhel, "Fraclab," www-rocq. inria. fr/fractales, May 2000
  16. D. L. Donoho, "De-noising by soft-thresholding", IEEE Trans. Information Theory, vol. 41, no. 3, pp. 613- 627, May1995. Reports/1992/denoisereleas e3. ps. Z.
  17. R. Yang, L. Yin, M. Gabbouj, J. Astola, and Y. Neuvo, "Optimal weighted median filters understructural constraints," IEEE Trans. Signal Processing vol. 43, pp. 591–604, Mar. 1995.
  18. Y. Q. Dong and S. F. Xu, "A new directional weighted median filter for removal of random-valued impulse noise,"IEEE Signal Processing Letters, 2007, vol. 14, no. 3, pp. 193–196.
  19. K. S. Srinivasan and D. Ebenezer, "A new fast and efficient decision based algorithm for removal of high density impulse noise," IEEE signal process, Lett. vol. 14, no. 3, pp. 189-192, March 2007.
  20. Kaveri A. P. and K. J. Amrutkar, "Median filtering frameworks and their application to image enhancement", IJAIEM, volume 3, issue 3, March 2014.
  21. K. Aiswarya, V. Jayaraj, and D. Ebenezer, "A new and efficient algorithm for the removal of high density salt and pepper noise.
  22. Dr. G. Ramachandra Reddy, A. Srinivas, M. Eswar Reddy and, T. Sunilkumar "Removal of high density impulse noise through modified non linear filter"2013
  23. S. Esakkirajan, T. VeeraKumar, Adabala N. Subramanyam and C. H. Premchand,"Removal of high density salt and pepper noise through modified based asymmetric trimmed median filter", IEEE Signal processing letter,vol. 18,no. 5,may 2011.
  24. Dr. G. Ramachandra Reddy, A. Srinivas, M. Eswar Reddy and, T. Sunilkumar "Removal of high density impulse noise through modified non linear filter"2013
  25. H. Ibrahim, "Adaptive switching median filter utilizing quantized window size to remove impulse noise from digital images," Asian Transactions on Fundamentals of Electronics, Communication and Multimedia, 2012, vol. 2, no. 1, pp. 1-6.
  26. Z Zhou, Cognition and Removal of Impulse Noise with Uncertainty. IEEE Trans. Image Process. 21(7), 3157–3167 (2012)
  27. Z Deng, Z Yin, Y Xiong, High probability impulse noise-removing algorithm based on mathematical morphology, IEEE Signal Process. Lett. 14(1), 31–34(2007).
  28. A Jourabloo, AH Feghahati, M Jamzad, New algorithms for recovering highly corrupted images with impulse noise. Scientia Iranica 19(6), (2012). doi:10. 1016/j. scient. 2012. 07. 016
  29. Majid, A. and Tariq Mahmood, M. ''A novel technique for removal of high density impulse noise from digital images'', IEEE International Conference on Emerging Technologies, ICET, Islamabad, Pakistan, pp. 139–143 (2010).
  30. PY Chen, CY Lien, An efficient edge-preserving algorithm for removal of salt-and-pepper noise. IEEE Signal Process. Lett 15, 833–836 (2008)
  31. X Zhang, Y Xiong, Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter. IEEE Signal Process. Lett. 16 (4), 295–298 (2009).
  32. "Fast restoration of natural images corrupted by high-density impulse noise" Hosseini and Marvasti EURASIP Journal on Image and Video Processing 2013, 2013:15 content/2013/1/15
  33. Dodda Shekar, Rangu Srikanth, "Removal of high density salt & pepper noise in noisy images using Decision Based Unsymmetric Trimmed Median Filter", IJCTT-vol. 2, issel-2011.
  34. R. C. Hardie and K. E. Barner, "Rank conditioned rank selection filters for signal restoration," IEEE Trans. Image Processing, vol. 3, pp. 192–206, Mar. 1994.
  35. Removal of high density salt and pepper noise in noisy image using Decision based unsymmetric trimmed median filter (DBUTMF) DoddaShekar#1, Rangu Srikanth*2#1 M. Tech in VLSI
  36. EngineeringJayamukhi Institute of Technology and Science Narsampet, Warangal, AP, India.
  37. DENG Xiuqin, XIONG and Yong PENG HONG, "A new kind of weighted median filtering algorithm used for image Processing," International Symposium on Information Science and Engineering. vol. 2, pp. 738-743, Dec. 20-22, 2008.
  38. "An enhanced non linear Adaptive filtering technique for removing high density salt and pepper noise "International Journal of Computer Applications (0975 – 8887) Volume 38– No. 11, January 2012 Muhammad mizanur rahman,Faisal Ahmed
  39. Ben 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.
  40. V. Naga Prudhvi Raj, T. Venkateswarlu, "Denoising of Medical Images Using Dual Tree Complex Wavelet Transform", Procedia Technology, Volume 4, 2012, Pages 238-244
Index Terms

Computer Science
Information Sciences

Keywords

Impulse Noise Digital Image Median Filter PSNR and MSE.

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