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Feature Edge-Detail Preservation of Random-Valued Impulse Noise in Images

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Trends in Wireless Communication and Information Security

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 740))

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Abstract

In this paper, we put forth a progressive, decision-based, two-phase image denoising algorithm for eliminating random-valued impulse noise from images. The manner in which this algorithm deals with noise is a completely pristine method when compared to the other existing image denoising algorithms. In the primary phase, the noise is dealt at a coarse level; in other words, the noisy pixels that are easily differentiable from the neighborhood are eliminated. In the secondary phase, fine-level image denoising is performed. In other words, the left-over fine scale noise in the detected corrupted pixels of the first phase, which cannot be straightforwardly differentiated from the surrounding pixels, is eliminated. In both the phases, separate mechanisms were followed to eliminate noise in the interior regions and edge regions. Hence, the algorithm is edge-detail preserving. Images with very high noise levels, in other words, with 70% noisy pixels were restored successfully. Speaking in terms of quantitative significant measures, the restored images in most cases were better than those of the other existing filters.

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References

  1. Gonzalez RC, Woods RE (2002) Digital image processing. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  2. Pratt WK (1975) Median filtering. Technical report, Image processing Institute, University of Southern California, Los Angeles, Sept 1975

    Google Scholar 

  3. Bovik A (2000) Handbook of image and video processing. Academic, New York

    MATH  Google Scholar 

  4. Huang TS, Yang GJ, Tang GY (1979) Fast two-dimensional median filtering algorithm. IEEE Trans Acoustics Speech Signal Process ASSP–1(1):13–18

    Google Scholar 

  5. Srinivasan KS, Ebenezer D (2007) A new fast and efficient decision based algorithm for removal of high-density impulse noises. IEEE Signal Process Lett 14(3):189–192

    Article  Google Scholar 

  6. Brownrigg D (1984) The weighted median filter. Commun Assoc Comput 807–818

    Google Scholar 

  7. Hwang H, Haddad RA (1995) Adaptive median filters: new algorithms and results. IEEE Trans Image Process 4(4):499–502

    Article  Google Scholar 

  8. Syamala Jaya Sree P, Kumar P, Siddavatam R, Verma R (2013) Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets. Signal Image Video Process 7(1):111–118

    Google Scholar 

  9. Chen T, Wu HR (2001) Space variant median filters for the restoration of impulse noise corrupted images. IEEE Trans Circuit Syst II Analog Digit Signal Process 48(8):784–789

    Google Scholar 

  10. Eng H-L, Ma K-K (2001) Noise adaptive soft-switching median filter. IEEE Trans Image Process 10(2):242–251

    Article  Google Scholar 

  11. Pok G, Liu J-C, Nair AS (2003) Selective removal of impulse noise based on homogeneity level information. IEEE Trans Image Process 12(1):85–92

    Article  Google Scholar 

  12. Chen T, Wu HR (2001) Adaptive impulse detection using center-weighted median filters. IEEE Signal Process Lett 8(1):1–3

    Article  Google Scholar 

  13. Windyga PS (2001) Fast impulsive noise removal. IEEE Trans Image Process 10(1):173–179

    Article  Google Scholar 

  14. Alajlan N, Kamel M, Jernigan E (2004) Detail preserving impulsive noise removal. Signal Process Image Commun 19:993–1003

    Article  Google Scholar 

  15. Abreu E, Lightstone M, Mitra SK, Arakawa K (1996) A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Trans Image Process 5(6):1012–1025

    Article  Google Scholar 

  16. Luo W (2005) A new efficient impulse detection algorithm for the removal of impulse noise. IEICE Trans Fundam E88-A(10):2579–2586

    Google Scholar 

  17. Syamala Jaya Sree P, Raj P, Kumar P, Siddavatam R, Ghrera SP (2013) A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines. Signal Image Video Process 7(6):1145–1157

    Google Scholar 

  18. Aizenberg I, Butakoff C, Paliy D (2005) Impulsive noise removal using threshold Boolean filtering based on the impulse detecting functions. IEEE Signal Process Lett 12(1):63–66

    Article  Google Scholar 

  19. Besdok E, Yksel ME (2005) Impulsive noise suppression from images with Jarque-Bera test based median filter. Int J Electron Commun 59:105–110

    Article  Google Scholar 

  20. Crnojevic V, Senk V, Trpovski Z (2004) Advanced impulse detection based on pixel-wise MAD. IEEE Signal Process Lett 11(7):589–592

    Google Scholar 

  21. Russo F (2004) Impulse noise cancellation in image data using a two-output nonlinear filter. Measurement 36:205–213

    Article  Google Scholar 

  22. Chen T, Ma K-K, Chen L-H (1999) Tri-state median-based filters in image denoising. IEEE Trans Image Process 8(12):1834–1838

    Article  Google Scholar 

  23. Luo W (2007) An efficient algorithm for the removal of impulse noise from corrupted images. Int J Electron Commun 61(8):551–555

    Article  Google Scholar 

  24. Petrovic N, Crnojevic V (2008) Universal impulse noise filter based on genetic programming. IEEE Trans Image Process 17(7):1109–1120

    Article  MathSciNet  Google Scholar 

  25. Jian Wu, Tang C (2011) PDE-based random-valued impulse noise removal based on new class of controlling functions. IEEE Trans Image Process 20(9):2428–2438

    Article  MathSciNet  Google Scholar 

  26. Ghanekar U, Singh AK, Pandey R (2010) A contrast enhancement based filter for removal of random valued impulse noise. IEEE Signal Process Lett 17(1):47–50

    Article  Google Scholar 

  27. Bodduna K, Siddavatam R (2012) A novel algorithm for detection and removal of random valued impulse noise using cardinal splines. In: 2012 Annual IEEE proceedings of India conference (INDICON), Dec 2012, pp 1003–1008

    Google Scholar 

  28. Bodduna K (2013) A novel algorithm for random-valued-impulse noise detection and removal using Chebyshev polynomial interpolation. In: Proceedings of second IEEE international conference on image information processing (ICIIP), Dec 2013, pp 410–415

    Google Scholar 

  29. Jayasree S, Bodduna K, Pattnaik PK, Siddavatam R (2014) An expeditious cum efficient algorithm for salt-and-pepper noise removal and edge-detail preservation using cardinal spline interpolation. J Vis Commun Image R 25:1349–1365

    Google Scholar 

  30. Unser M (1999) Splines: a perfect fit for signal and image processing. IEEE Signal Process Mag 16(6):24–38

    Article  Google Scholar 

  31. Unser M, Aldroubi A, Eden M (1991) Fast B-spline transforms for continuous image representation and interpolation. IEEE Trans Pattern Anal Mach Intell 13(3):277–285

    Article  Google Scholar 

  32. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering, KIIT University, Bhubaneswar, India

    Patitapaban Rath & Pradeep Kumar Mallick

  2. VVIT, Guntur, India

    Rajesh Siddavatam

Authors
  1. Patitapaban Rath
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  2. Rajesh Siddavatam
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  3. Pradeep Kumar Mallick
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Corresponding author

Correspondence to Patitapaban Rath .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Amity University, Ranchi, Jharkhand, India

    Mithun Chakraborty

  2. Amity University, Ranchi, Jharkhand, India

    Raman Kr. Jha

  3. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Sikkim Manipal Institute of Technology, Majitar, Sikkim, India

    Samarendra Nath Sur

  5. North Eastern Hill University, Shillong, Meghalaya, India

    Debdatta Kandar

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Rath, P., Siddavatam, R., Mallick, P.K. (2021). Feature Edge-Detail Preservation of Random-Valued Impulse Noise in Images. In: Chakraborty, M., Jha, R.K., Balas, V.E., Sur, S.N., Kandar, D. (eds) Trends in Wireless Communication and Information Security. Lecture Notes in Electrical Engineering, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-33-6393-9_15

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  • DOI: https://doi.org/10.1007/978-981-33-6393-9_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6392-2

  • Online ISBN: 978-981-33-6393-9

  • eBook Packages: EngineeringEngineering (R0)

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