Two methods of adaptive median filtering of impulse noise in images
Chervyakov N.I., Lyakhov P.A., Orazaev A.R.

 

North-Caucasus Federal University, Stavropol, Russia

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Abstract:
Two new methods of adaptive median filtering of impulse noise in images are proposed in the paper. The first method is based on the joint application of iterative processing and transformation of the result of median filtering using the Lorentzian function. The second method uses alternative masks of median filter, calculated using the Euclidean metric. This approach has made it possible to reduce the size of the processed area without the loss in quality for low-intensity noise. The experimental part of the article shows the results of comparison of the performance of the proposed methods with the known methods. We used three different images distorted by impulse noise with pixel distortion probabilities ranging from 1 % up to 99 %. The numerical evaluation of the quality of image denoising based on the peak signal to noise ratio (PSNR) and the structural similarity (SSIM) has shown that the proposed method shows a better result of processing in all the cases considered, as compared with the known approaches. The results obtained in the paper may find wide practical applications when processing satellite and medical imagery, geophysical data, and in other areas of digital image processing.

Keywords:
image processing, noise in imaging systems, impulse noise, filters, median filter, adaptive filter.

Citation:
Chervyakov NI, Lyakhov PA, Orazaev AR. Two methods of adaptive median filtering of impulse noise in images. Computer Optics 2018; 42(4): 667-678. DOI: 10.18287/2412-6179-2018-42-4-667-678.

References:

  1. Gonzalez RC, Woods RE. Digital image processing. Upper Saddle River: Pearson Prentice Hall; 2007. ISBN: 978-0-13-168728-8.
  2. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. Upper Saddle River, NJ: Prentice-Hall, Inc; 2010: 344. ISBN: 978-0-9820854-0-0.
  3. Bovik AC. Handbook of image and video processing. Orlando, FL: Academic Press; 2010: 1372. ISBN: 978-0-12-119792-6.
  4. Tukey JW. Exploratory data analysis. Reading, MA: Pearson; 1977. ISBN: 978-0-201-07616-5.
  5. Ko S-J, Lee YH. Center weighted median filters and their applications to image enhancement. IEEE Transactions on Circuits Systems; 1991, 38(9): 984-993. DOI: 10.1109/31.83870.
  6. Wang Z, Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits and Systems II 1999; 46(1): 78-80. DOI: 10.1109/82.749102.
  7. Hwang H, Haddad RA. Adaptive median filters: new algorithms and results. IEEE Transactions on Image Processing 1995; 4(4): 499-502. DOI: 10.1109/83.370679.
  8. Lu C-T, Chen Y-Y, Wang L-L, Chang C-F. Removal of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size-window. Pattern Recognition Letters 2016; 80: 188-199. DOI: 10.1016/j.patrec.2016.06.026.
  9. Fabijanska A, Sankowski D. Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images. IET Image Processing 2011; 5(5): 472-480. DOI: 10.1049/iet-ipr.2009.0178.
  10. Ng P-E, Ma K-K. A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans Image Process 2006; 15(6): 1506-1516. DOI: 10.1109/TIP.2005.871129.
  11. Peixuan Z, Fang L. A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE Signal Processing Letters 2014; 21(10): 1280-1283. DOI: 10.1109/LSP.2014.2333012.
  12. Roy A, Singha J, Manam L, Laskar RH. Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images. IET Image Processing 2017; 11(6): 352-361. DOI: 10.1049/iet-ipr.2016.0320.
  13. Toh KKV, Isa NAM. Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Processing Letters 2010; 17(3): 281-284. DOI: 10.1109/LSP.2009.2038769.
  14. Hsieh MH, Cheng FH, Shie MC, Ruan SJ. Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images. Engineering Applications of Artificial Intelligence 2013; 26(4): 1333-1338. DOI: 10.1016/j.engappai.2012.10.012.
  15. Vijaykumar VR, Vanathi PT, Kanagasabapathy P, Ebenezer D. High density impulse noise removal using robust estimation based filter. IAENG International Journal of Computer Science 2008; 35(3): 259-266.
  16. Jourabloo A, Feghahati AH, Jamzad M. New algorithms for recovering highly corrupted images with impulse noise. Scientia Iranica 2012; 19(6): 1738-1745. DOI: 10.1016/j.scient.2012.07.016.
  17. Chen Y, Zhang Y, Yang , Shu H, Luo L, Coatrieux JL, Feng Q. Structure-Adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression. IEEE Transactions on Circuits and Systems for Video Technology 2018; 28(2): 414-427. DOI: 10.1109/TCSVT.2016.2615444.
  18. Srinivasan KS, Ebenezer D. A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Signal Processing Letters 2007; 14(3): 189-192. DOI: 10.1109/LSP.2006.884018.
  19. Brownrigg DRK. The weighted median filter. Communications of the ACM 1984; 27(8): 807-818. DOI: 10.1145/358198.358222.
  20. Yin L, Yang R, Gabbouj M, Neuvo Y. Weighted median filters: a tutorial. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 1996; 43(3): 157-192. DOI: 10.1109/82.486465.
  21. Zhou H, Zeng B, Neuvo Y. Weighted FIR median hybrid filters for image processing. Proc Int Conf Circuits and Syst 1991: 793-796. DOI: 10.1109/CICCAS.1991.184480.
  22. Chen T, Wu HR. Adaptive impulse detection using center-weighted median filters. IEEE Signal Process Lett 2001; 8(1): 1-3. DOI: 10.1109/97.889633.
  23. Chan RH, Hu Chen, Nikolova M. An iterative procedure for removing random-valued impulse noise. IEEE Signal Process Lett 2004; 11(12): 921-924. DOI: 10.1109/LSP.2004.838190,
  24. Black MJ, Rangarajan A. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. Int J Comput Vision 1996; 19(1): 57-91. DOI: 10.1007/BF00131148.
  25. Jahne B. Digital image processing. Berlin, Heidelberg: Springer; 2005. ISBN: 978-3-540-24035-8.
  26. Jelodari PT, Kordasiabi MP, Sheikhaei S, Forouzandeh B. FPGA implementation of an adaptive window size image impulse noise suppression system. Journal of Real-Time Image Processing; 2017: 1-12. DOI: 10.1007/s11554-017-0705-4.
  27. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on image processing; 2004, 13(4): 600-612. DOI: 10.1109/TIP.2003.819861.
  28. Bitbucket. Source: <https://bitbucket.org/anzor_orazaev/mmm/src/master/>.
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