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3D-generalization of impulse noise removal method for video data processing

N.I. Chervyakov 1, P.A. Lyakhov 11, A.R. Orazaev 1

1 North-Caucasus Federal University, 355009, Russia, Stavropol, Pushkin street 1

 PDF, 1064 kB

DOI: 10.18287/2412-6179-CO-577

Pages: 92-100.

Full text of article: Russian language.

Abstract:
The paper proposes a generalized method of adaptive median impulse noise filtering for video data processing. The method is based on the combined use of iterative processing and transformation of the result of median filtering based on the Lorentz distribution. Four different combinations of algorithmic blocks of the method are proposed. The experimental part of the paper presents the results of comparing the quality of the proposed method with known analogues. Video distorted by impulse noise with pixel distortion probabilities from 1% to 99% inclusive was used for the simulation. Numerical assessment of the quality of cleaning video data from noise based on the mean square error (MSE) and structural similarity (SSIM) showed that the proposed method shows the best result of processing in all the considered cases, compared with the known approaches. The results obtained in the paper can be used in practical applications of digital video processing, for example, in systems of video surveillance, identification systems and control of industrial processes.

Keywords:
digital video processing, adaptive filtering, median filter.

Citation:
Chervyakov NI, Lyakhov PA, Orazaev AR. 3D-generalization of impulse noise removal method for video data processing. Computer Optics 2019; 44(1): 92-100. DOI: 10.18287/2412-6179-CO-577.

Acknowledgements:
This work was supported by the Government of the Russian Federation (state order no. 2.6035.2017/BCh), the Russian Foundation for Basic Research (project no. 18-07- 00109 A, №19-07-00130 А and №18-37-20059 mol-a-ved), and by the Presidential Grant of the Russian Federation (project no. SP-2245.2018.5).

References:
  1. Bovik AC. Handbook of image and video processing. Orlando, FL: Academic Press; 2010. ISBN: 978-0-12- 119792-6.
  2. Gonzalez RC, Woods RE. Digital image processing. Upper Saddle River: Pearson Prentice Hall; 2018. ISBN: 978-0-13-335672-4.
  3. Bovik AC. The essential guide to image processing. Orlando, FL: Academic Press, 2010.
  4. Chervyakov NI, Lyakhov PA, Orazaev AR, Valueva MV. Efficiency analysis of the image impulse noise cleaning using median filters with weighted central element. IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) 2017: 141-146.
  5. 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.
  6. Chervyakov NI, Lyakhov PA, Orazaev AR. Two methods of adaptive median impulse noise filtering on images. Computer Optics 2018; 42(4): 667-678.
  7. Bovik AC. The essential guide to video processing. Sun Diego, CA: Academic Press; 2009.
  8. Aiswarya K, Jayaraj V, Ebenezer D. A new and efficient algorithm for the removal of high density salt and pepper noise in images and videos. IEEE Second International Conference on Computer Modeling and Simulation 2010; 4: 409-413.
  9. Balasubramanian S, Ebenezer D. An efficient non-linear cascade filtering algorithm for removal of high density salt and pepper noise in image and video sequence. IEEE International Conference on Control, Automation, Communication and Energy Conservation 2009: 1-6.
  10. Tang M, Chen X, Wen J, Han Y. Hadamard transform-based optimized HEVC video coding. IEEE Transactions on Circuits and Systems for Video Technology 2019; 29(3): 827-839.
  11. Vayalil NC, Paul M, Kong Y. A residue number system hardware design of fast-search variable-motion-estimation accelerator for HEVC/H.265. IEEE Transactions on Circuits and Systems for Video Technology 2017, 29(2): 572-581.
  12. Chen Y, Zhang Y, Shu H, Yang J, Luo L. 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.
  13. Bucha VV, Ignatov AK. Method for improving disparity map and device for implementing method [In Russian]. Pat RF of Invent N RU2382406C1 of February 20, 2010; Russian Bull of Inventions N5.
  14. Fabijańska 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.
  15. 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. Patt Recogn Let 2016; 80: 188-199. DOI: 10.1016/j.patrec.2016.06.026.
  16. Tukey JW. Exploratory data analysis. Reading, MA: Pearson; 1977. ISBN: 978-0-201-07616-5.
  17. Arce GR. Multistage order statistic filters for image sequence processing. IEEE Transactions on Signal Processing 1991; 39(5): 1146-1163.
  18. 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.
  19. 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.
  20. Bitbucket. Source: áhttps://bitbucket.org/anzor_orazaev/ testvideo/src/master/ñ.
  21. 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: Analog and Digital Signal
  22. Processing 1999; 46(1): 78-80. DOI: 10.1109/82.749102. 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.

 

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