Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Four stage median-average filter for healing high density salt and pepper noise corrupted images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper introduces a novel four stage filter algorithm to ameliorate images corrupted by very high density salt-and-pepper noise. The proposed algorithm exhibits two parallel trimmed median filters (TMF) at the initial stage followed by a masking logic that selects denoised pixel based on the priority. To reduce the blurring effect, higher priority is given to TMF with small window size. In the absence of noise-free pixels, the current extreme pixel is left unchanged at the first stage. Further, the denoising of unprocessed extreme pixels is done with TMF of large size window at the second stage. The remaining noisy pixels are improved by the running average filter at the third stage. Finally, the last stage handles the noisy pixels at the boundary and rare scenario. Since the proposed filter utilized non-extreme pixels to estimate denoinsed pixels value, it effectively eliminates salt and pepper noise along with better edge preservation. The performance analysis of the proposed filter is carried out with various benchmark images for varying noise density. The experimental results demonstrate on an average improvement of 2.09 dB (0.018) and 1.06 dB (0.0478) of PSNR (SSIM) respectively for wide (10% - 90%) and very-high (90% - 98%) noise density ranges over state-of-the-art filters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Ahmed F, Das S (2013) Removal of high-density salt-and-pepper noise in images with an iterative adaptive fuzzy filter using alpha-trimmed mean. IEEE Trans Fuzzy Syst 22(5):1352–1358

    Article  Google Scholar 

  2. Aiswarya K, Jayaraj V, Ebenezer D (2010) A new and efficient algorithm forthe removal of high density salt and pepper noise in images and videos. In: 2010 Second international conference on computer modeling and simulation, vol 4. IEEE, pp 409–413

  3. Azhar M, Dawood H, Dawood H, Choudhary GI, Bashir AK, Chauhdary SH (2019) Detail-preserving switching algorithm for the removal ofrandom-valued impulse noise. J Ambient Intell Humaniz Comput 10(10):3925–3945

    Article  Google Scholar 

  4. Balasubramanian G, Chilambuchelvan A, Vijayan S, Gowrison G (2016) Anextremely fast adaptive high-performance filter to remove salt and noise using overlapping medians in images. The Imaging Science Journal 64(5):241–252

    Article  Google Scholar 

  5. Balasubramanian G, Chilambuchelvan A, Vijayan S, Gowrison G (2016) Probabilistic decision based filter to remove impulse noise using patchelse trimmed median. AEU-International Journal of Electronics and Communications 70(4):471–481

    Article  Google Scholar 

  6. Burger HC, Schuler CJ, Harmeling S (2012) Image denoising: Can plain neural networks compete with bm3d?. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 2392–2399

  7. Deivalakshmi S, Palanisamy P (2010) Improved tolerance based selective arithmetic mean filter for detection and removal of impulse noise. In: 2010 5th International conference on industrial and information systems. IEEE, pp 309–313

  8. Deivalakshmi S, Palanisamy P (2016) Removal of high density salt and peppernoise through improved tolerance based selective arithmetic mean filtering with wavelet thresholding. AEU-International Journal of Electronics and Communications 70(6):757–776

    Article  Google Scholar 

  9. Erkan U, Gökrem L. (2018) A new method based on pixel density in salt and pepper noise removal. Turkish Journal of Electrical Engineering & Computer Sciences 26(1):162–171

    Article  Google Scholar 

  10. Erkan U, Gökrem L, Enginoğlu S (2018) Different applied median filter in salt and pepper noise. Computers & ElectricalEngineering 70:789–798

    Google Scholar 

  11. Esakkirajan S, Veerakumar T, Subramanyam AN, PremChand C (2011) Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Processing Letters 18 (5):287–290

    Article  Google Scholar 

  12. Faragallah OS, Ibrahem HM (2016) Adaptive switching weighted median filter framework for suppressing salt-and-pepper noise. AEU-International Journal of Electronics and Communications 70(8):1034–1040

    Article  Google Scholar 

  13. Garg B (2020) Restoration of highly salt-and-pepper-noise-corrupted images using novel adaptive trimmed median filter. Signal, Image and Video Processing, https://doi.org/10.1007/s11760-020-01695-3

  14. Gonzalez RC, Woods RE (2018) Digital image processing, 4th edn. Pearson, London

    Google Scholar 

  15. Kandemir C, Kalyoncu C, Toygar Ö (2015) A weighted mean filter with spatial-bias elimination for impulse noise removal. Digital Signal Processing 46:164–174

    Article  MathSciNet  Google Scholar 

  16. Karthik B, Kumar TK, Vijayaragavan S, Sriram M (2020) Removal of high density salt and pepper noise in color image through modified cascaded filter. J Ambient Intell Humaniz Comput, 1–8

  17. Liang S-F, Lu S-M, Chang J-Y, Lin C-T (2008) A novel two-stage impulse noise removal technique based on neural networks and fuzzy decision. IEEE Trans Fuzzy Syst 16(4):863–873

    Article  Google Scholar 

  18. Lin T. -C. (2012) Decision-based filter based on SVM and evidence theory for image noise removal. Neural Comput and Applic 21(4):695–703

    Article  Google Scholar 

  19. Lu C-T, Chen Y-Y, Wang L-L, Chang C-F (2016) Removal of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size window. Pattern Recogn Lett 80:188–199

    Article  Google Scholar 

  20. Mafi M, Martin H, Cabrerizo M, Andrian J, Barreto A, Adjouadi M (2018) A comprehensive survey on impulse and gaussian denoising filters for digital images. Signal Processing

  21. Mafi M, Rajaei H, Cabrerizo M, Adjouadi M (2018) A robust edge detectionapproach in the presence of high impulse noise intensity through switching adaptive median and fixed weighted mean filtering. IEEE Trans Image Process 27(11):5475–5490

    Article  MathSciNet  Google Scholar 

  22. Meher SK, Singhawat B (2014) An improved recursive and adaptive medianfilter for high density impulse noise. AEU-International Journal of Electronics and Communications 68(12):1173–1179

    Article  Google Scholar 

  23. Nair MS, Shankar V (2013) Predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector. SIViP 7 (6):1041–1070

    Article  Google Scholar 

  24. Pitas I, Venetsanopoulos AN (2013) Nonlinear digital filters: principles and applications. Springer Science & Business Media, vol 84

  25. Roy A, Laskar RH (2017) Non-casual linear prediction based adaptive filterfor removal of high density impulse noise from color images. AEU-International Journal of Electronics and Communications 72:114–124

    Article  Google Scholar 

  26. Roy A, Singha J, Devi SS, Laskar RH (2016) Impulse noise removal using SVM classification based fuzzy filter from gray scale images. Signal Process 128:262–273

    Article  Google Scholar 

  27. Sanaee P, Moallem P, Razzazi F (2019) An interpolation filter based on natural neighbor galerkin method for salt and pepper noise restoration with adaptive size local filtering window, Signal. Image and Video Processing 13(5):895–903

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Turkmen I (2016) The ann based detector to remove random-valued impulse noise in images. J Vis Commun Image Represent 34:28–36

    Article  Google Scholar 

  30. Veerakumar T, Esakkirajan S, Vennila I (2014) Recursive cubic spline interpolation filter approach for the removal of high density salt-and-peppernoise. Signal Image and Video Processing 8(1):159–168

    Article  Google Scholar 

  31. Vijaykumar V, Mari GS, Ebenezer D (2014) Fast switching based median–mean filter for high density salt and pepper noise removal. AEU-International Journal of Electronics and Communications 68(12):1145–1155

    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 

  33. Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142–3155

    Article  MathSciNet  Google Scholar 

  34. Zhang K, Zuo W, Gu S, Zhang L (2017) Learning deep CNN denoiser prior for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3929–3938

Download references

Author information

Authors and Affiliations

  1. ECED, Thapar Institute of Engineering and Technology, Patiala, 147001, India

    Bharat Garg

  2. ABV-Indian Institute of Information Technology and Management Gwalior, Gwalior, 474015, India

    K. V. Arya

Authors
  1. Bharat Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. V. Arya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bharat Garg.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garg, B., Arya, K.V. Four stage median-average filter for healing high density salt and pepper noise corrupted images. Multimed Tools Appl 79, 32305–32329 (2020). https://doi.org/10.1007/s11042-020-09557-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09557-3

Keywords

Search

Navigation