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A Novel Mean Median Filter for Noise and Artifacts Suppression from Digital Images

by Geeta Hanji, M.v.latte
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 114 - Number 17
Year of Publication: 2015
Authors: Geeta Hanji, M.v.latte
10.5120/20070-1989

Geeta Hanji, M.v.latte . A Novel Mean Median Filter for Noise and Artifacts Suppression from Digital Images. International Journal of Computer Applications. 114, 17 ( March 2015), 20-26. DOI=10.5120/20070-1989

@article{ 10.5120/20070-1989,
author = { Geeta Hanji, M.v.latte },
title = { A Novel Mean Median Filter for Noise and Artifacts Suppression from Digital Images },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 114 },
number = { 17 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume114/number17/20070-1989/ },
doi = { 10.5120/20070-1989 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:53:02.189018+05:30
%A Geeta Hanji
%A M.v.latte
%T A Novel Mean Median Filter for Noise and Artifacts Suppression from Digital Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 114
%N 17
%P 20-26
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A novel mean-median filter is proposed for the suppression of impulse noise and various artifacts from the digital images. Leading Diagonal Sorting Algorithm is used with the fixed 3x3 size working window to compute the median. Truncated mean is computed by defining the boundaries and truncating the pixel values in the filtering window that fall outside the defined boundary. Noise detection is carried in two steps: In the first step the 'reference pixel' is tested for the presence of impulses with Min-Max detection strategy. In the second stage of detection, edge preserving unique criteria is employed to further classify the pixel under test as 'noisy' or 'edge belonged'. This intelligent edge preserving decision criterion decides whether the test pixel deserves the restoration or not and facilitates the restoration of the noisy pixel either by the 'truncated mean' or by the 'window median' based on the decision threshold. Performance of the proposed filter algorithm is studied on a large number of images with varying amounts of salt and pepper noise and several types of artifacts and their combinations. Simulation results prove that the proposed algorithm (PA) effectively suppresses the high density salt and pepper noise (SPN) and in addition, it performs excellent in suppressing image artifacts such as strip lines (both white and black), drop lines (both white and black), missing bands and noise blotches. The novelty of the proposed mean-median filter lies in the fact that it attempts to simultaneously suppress impulsive noise and artifacts with good edge preservation (as the maximum size of the filtering window is restricted to '3×3' only) and also because a two stage detection mechanism is employed. The goodness of the proposed algorithm lies in replacing several independent filter schemes required for suppression of noise and artifacts of several types without much distorting the vital features of the image under test. The performance evaluation of the proposed algorithm is done in terms visual appearance and quantitative measures such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Image Enhancement Factor (IEF) and the Computation Time (CT). Simulation results are compared with other state of art algorithms to derive the meaningful conclusions.

References
  1. A. Bovik, Handbook of Image and Video Processing, Academic Press, 2000.
  2. S. M. Shahrokhy,Visual and Statistical Quality Sssessment and Improvement of Remotely Sensed Images, Proceedings of the 20th Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS'04), Turkey, July 2004.
  3. S. Manikandan and D. Ebenezer,A Nonlinear Decision-based Algorithm for Removal of Strip lines, Drop lines, Blotches, Band missing and Impulses in images and videos. Proceedings of EURASIP Journal on Image and Video Processing, Article ID 485921, 2008.
  4. A. U. Silva and L. Corte-Real, Removal of Blotches and Line scratches from Film and Video Sequences using a Digital Restoration Chain, Proceedings of the IEEEE URASIP workshop on Nonlinear Signal and Image Processing (NSIP ' 99),pp. 826- 829,Antalya,Turkey,June 1999.
  5. A. Kokaram, Detection and Removal of Line Scratches in Degraded Motion Picture Sequences, Proceedings of the 8th European Signal Processing Conference (EUSIPCO' 96), vol. 1, pp. 5-8, Trieste, Italy, September 1996.
  6. Sonali R. M, Nileshsingh V. T. , A Comparative Study of Image Filtering on Various Noisy pixels, International Journal of Image Processing and Vision Sciences, ISSN (Print): 2278 – 1110, Volume-1, Issue-2, 2012.
  7. Sin Hoong Teoh, Boon Tatt Koik, and Haidi Ibrahim, Exploration of Current Trend on Median Filtering Methods Utilized in Digital Grayscale Image Processing, International Journal of Materials, Mechanics and Manufacturing, Vol. 1, No. 1, February 2013.
  8. Geeta Hanji and M. V. Latte, A New Threshold Based Median Filtering Technique for Salt and Pepper Noise Removal,(ICDIP-2010), 2010 Proceedings of SPIE, Vol. 7546, PP754639-754639-10,Singapur, 2010, DOI: 10. 1117/12. 856333. http://dx. doi. org/10. 1117/12. 856333.
  9. Geeta Hanji and M. V. Latte, Detail Preserving Fast Median Based Filter," Journal of Advanced Computer Science and Technology, 1(4) (2012) 195-206 © Science Publishing Corp oration,www. sciencepubco. com/index. php/JACSTarticle/view
  10. Geeta Hanji, M. V. Latte, A New Impulse Noise Detection and Filtering Algorithm, Image Processing &Communications Life Sciences, ISSN (Print): 2278 – 1110, Volume-1, Issue-2, 2012, IPC, The Journal of University of Technology and Life Sciences in Bydgoszcz, Vol. 16, no. 1-2, pp. 43-48, DOI: 10. 2478/v10248-012-0004-4.
  11. V. R. Vijaykumar, P. Jothibasu, Decision based adaptive median filter to remove blotches, scratches, streaks, stripes andimpulse noise in images. Proceedings of IEEE Int. conference on Image Processing , pp. 117-120, Sep 2010.
  12. Suresh, Mangalam, Removal of different artifacts and impulse noise in grey images, Journal of Theoretical and Applied Information Technology ISSN: 1992-8645.
  13. S. K. Satpathy, S. Panda, K. K. Nagwanshi, S. K. Nayak and C. Ardil, Adaptive Non-linear Filtering Technique for Image Restoration International Journal of Electrical and Computer Engineering 5:1 2010.
  14. K. Vasanth,V. Jawahar senthilkumar, Novel Scheme for removal of Blotches, Black bands, Streaks and Impulse Noise in grey images, Indian Journal of Computer Science and Engineering (IJCSE), ISSN : 0976-5166 Vol. 3 No. 4 Aug-Sep 2012.
  15. Geeta Hanji, M. V. Latte, N. M. Shweta , An Improved Nonlinear Decision Based Algorithm for Removal of Blotches and Impulses in Gray scale Images. Proceedings of the Second International Conference,ACCT,ROHTAK,Haryana.
  16. P. Tamilselvam, M. V. Mahesh and G. Prabhu,Blotches and impulse removal in colour scale images using non-linear decision based algorithm, International journal of scientific research publications,vol. 3, no. 4, pp. 1-5, 2013.
  17. Kiruthika. D & Sarathbabu. R An Improved Nonlinear Decision based Algorithm for Removal of Blotches and Impulses in Color Images, International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE) ISSN (Print) : 2278-8948, Volume-2, Issue-3, 2013.
  18. Xudong Jiang, Iterative Truncated Arithmetic Mean Filter and its Properties, Transactions on Image Processing, Vol. 21, No. 4, April 2012.
  19. A. Rajamani, V. Krishnaveni, Impulse Denoising Algorithm for Gray and RGB Images, International Journal of Computer Applications (0975 – 8887) Volume 70– No. 2, May 2013.
  20. Nair, Rajgiri, Revathy, Tatavarti, An Improved Decision-Based Algorithm for Impulse Noise Removal, Congress on Image and Signal Processing, 2008. CISP 08. (Volume: 1) 426- 431, Print ISBN: 978-0-7695-3119-9, IEEE publisher.
  21. S. Esakkirajan, T. Veerakumar, Adabala, Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetrical Trimmed Median Filter, IEEE Signal Processing Letters, Vol. 18, No. 5, May 2011. http://dx. doi. org/10. 1109/LSP. 2011. 2122333.
  22. Vasant, Jawahar, A Decision based Trimmed Midpoint Algorithm for the Removal of High Density SPN. Journal of Theoretical and Applied Information Technology, August 2012. Vol. 42 No. 2 © 2005 - 2012 JATIT & LLS. ISSN: 1992-8645 . E-ISSN: 1817-3195.
  23. A. Jourabloo , A. H. Feghahati, M. Jamzad, New algorithms for recovering highly corrupted images withImpulse noise, Scientia Iranica Transactions D: Computer Science & Engineering and Electrical Engineering Scientia Iranica D (2012) 19 (6), 1738–1745.
Index Terms

Computer Science
Information Sciences

Keywords

Noise Artifacts Sorting Truncated Mean Impulse noise Strip Lines Blotches Drop lines Missing bands PSNR MSE Run time IEF.

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