Skip to main content
SpringerLink
Log in

A new cluster based adaptive fuzzy switching median filter for impulse noise removal

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

Abstract

A new clustering technique based on most allied directional neighbors is proposed to suppress low and high-density impulse noise from digital images. Most allied neighbors exhibit a vital role in estimation as well restoration of appropriate gray level value of corrupted pixels. In first phase, most allied directional neighbors, i.e., pixels directly attached to central pixel and the directional pixels (horizontal, vertical and two diagonal directions) next to attached pixels in the processing window are partitioned into two equal size clusters based on gradient values. Cluster with a minimum sum of gradient values (most similar neighbors) and the one with relatively large gradient values are passed to fuzzy inference system to infer the current pixel to be noisy-free, edge or a noisy. In second phase, a switching technique opts one of the three options depending upon fuzzy membership degrees and local information to restore the corrupted pixel value. A non-parametric approach based on local information for dynamic threshold setting using fuzzy logic makes the proposed filter computationally effective and adaptive to process a large number of images without user-defined parameters. The proposed algorithm is simple to implement and simulation results based on well know quantitative measures indicate the supremacy of the proposed filter for random-valued impulse noise as well as salt and peppers noise.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Arce GR, Paredes JL (2000) Recursive weighted median filters admitting negative weights and their optimization. IEEE Trans Signal Process 48(3):768–779

    Article  Google Scholar 

  2. Awad AS (2011) Standard deviation for obtaining the optimal direction in the removal of impulse noise. IEEE Signal Process Lett 18(7):407–410

    Article  Google Scholar 

  3. Bilal M, Hussain A, Jaffar MA, Choi T-S, Mirza AM (2014) Estimation and optimization based ill-posed inverse restoration using fuzzy logic. Multimedia Tools and Applications 69(3):1067–1087

    Article  Google Scholar 

  4. Brownrigg DRK (1984) The weighted median filter. ACM Commun 27(8):807–818

    Article  Google Scholar 

  5. Chen CLP, Li H, Wei Y, Xia T, Tang YY (2014) A local contrast method for small infrared target detection. IEEE Trans Geosci Remote Sens 52(1):574–581

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Dong Y, Xu S (2007) A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Process Lett 14(3):193–196

    Article  Google Scholar 

  8. Florencio DAF, Schafer RW (1994) Decision-based median filter using local signal statistic. Proc SPIE 2308:268–275

    Article  Google Scholar 

  9. Garnett R, Huegerich T, Chui C, He W (2005) A universal noise removal algorithm with an impulse detector. IEEE Trans Image Process 14(11):1747–1754

    Article  Google Scholar 

  10. 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 

  11. Gonzalez RC, Woods RE (2006) Digital Image Processing, 3rd edn. Prentice-Hall, USA

  12. Gui J, Tao D, Sun Z, Luo Y, You X, Tang YY (2014) Group sparse multi view patch alignment framework with view consistency for image classification, IEEE Trans. Image Process 23(7):3126–3137

    Article  MathSciNet  Google Scholar 

  13. Gupta V, Chaurasia V, Shandilya M (2015) Random-valued impulse noise removal using adaptive dual threshold median filter. J Vis Commun Image R 26:296–304

    Article  Google Scholar 

  14. Habib M, Rasheed S, Hussain A, Ali M (2015) Random value impulse noise removal based on most similar neighbours. 13th international conference on frontiers of information technology 329–333

  15. Habib M, Hussain A, Choi TS (2015) Adaptive threshold based fuzzy directional filter using background information. Appl Soft Comput 29:471–478

    Article  Google Scholar 

  16. Habib M, Hussain A, Rasheed S, Ali M (2016) Adaptive fuzzy inference system based directional median filer for impulse noise removal. Int J Electron Commun (AEÜ) 70:689–697

    Article  Google Scholar 

  17. Hussain A, Bhatti SM, Jaffar MA (2012) Fuzzy based impulse noise reduction method. Multimedia Tools and Applications 60(3):551–571

    Article  Google Scholar 

  18. Hwang H, Haddad RA (1995) Adaptive median filters: new algorithms and results. IEEE Trans Image Process 4(4):99–502

    Article  Google Scholar 

  19. Kang C-C, Wang W-J (2009) Fuzzy reasoning-based directional median filter design. Signal Process 89:344–351

    Article  MATH  Google Scholar 

  20. Kerre EE (1998) Fuzzy sets and approximate reasoning. Xian Kiaotong University Press. Xi'an, Shaanxi, China

    Google Scholar 

  21. Ko SJ, Lee YH (1991) Center weighted median filters and their applications to image enhancement. IEEE Trans Circ Syst 38(9):984–993

    Article  Google Scholar 

  22. Li Z, Liu G, Xu Y, Cheng Y (2014) Modified directional weighted filter for removal of salt & pepper noise. Pattern Recog Letters 40:113–120

    Article  Google Scholar 

  23. Lin CH, Tsai JS, Chiu CT (2010) Switching bilateral filter with a texture/noise detector for universal noise removal. IEEE Trans Image Process 19(9):2307–2320

    Article  MathSciNet  MATH  Google Scholar 

  24. Liu L, Chen CLP, Zhou Y, You X (2015) A new weighted mean filter with two phase detector for removing impulse noise. Inf Sci 315:1–16

    Article  MathSciNet  Google Scholar 

  25. Lu CT, Chou TC (2012) Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter. Pattern Recongnit Lett 33(10):1287–1295

    Article  Google Scholar 

  26. Ma K-K, Chen T, Chen L-H (1999) Tri-state median filter for image denoising. IEEE Signal Process Lett 8(12):1834–1838

    Google Scholar 

  27. Nodes T, Gallagher NC Jr (1982) Median filters: some modifications and their properties. IEEE Trans Acoust Speech Signal Process 30(5):739–746

    Article  Google Scholar 

  28. Roy A, Laskar RH (2017) Non-casual linear prediction based adaptive filter for removal of high density impulse noise from color images. Int J Electron Commun (AEÜ) 72:114–124

    Article  Google Scholar 

  29. Schulte S, Nachtegael M, Witte V, Weken DV, Kerre EE (2006) A fuzzy impulse noise detection and reduction method. IEEE Trans on Image Process 15(5):1153–1162

    Article  Google Scholar 

  30. Schulte S, Witte V, Nachtegael M, Weken DV, Kerre EE (2006) Fuzzy two-step filter for impulse noise reduction from color images. IEEE Trans on Image Process 15(11):3568–3579

    Article  Google Scholar 

  31. Schulte S, de Witte V, Nachtegael M, Weken DV, Kerre EE (2007) Fuzzy random impulse noise reduction method. Fuzzy Sets Syst 158(3):270–283

    Article  MathSciNet  Google Scholar 

  32. Toh KKV, Isa NAM (2010) Cluster-based adaptive fuzzy switching median filter for universal impulse noise reduction. IEEE Trans Consum Electron 56(4):2560–2568

    Article  Google Scholar 

  33. Turkmen I (2013) A new method to remove random-valued impulse noise in images. Int J Electron Commun (AEÜ) 67:771–779

    Article  Google Scholar 

  34. Xiong B, Yin Z (2012) A universal denoising framework with a new impulse detector and nonlocal means. IEEE Trans Image Process 21(4):1663–1675

    Article  MathSciNet  Google Scholar 

  35. Zhang J (2010) An efficient median filter based method for removing random valued impulse noise. Digit Signal Process 20(4):1010–1018

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Software Engineering, International Islamic University, Islamabad, Pakistan

    Ayyaz Hussain & Muhammad Habib

  2. College of Computing & Informatics, Saudi Electronic University, Riyadh, Kingdom of Saudi Arabia

    Ayyaz Hussain

Authors
  1. Ayyaz Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Habib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayyaz Hussain.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hussain, A., Habib, M. A new cluster based adaptive fuzzy switching median filter for impulse noise removal. Multimed Tools Appl 76, 22001–22018 (2017). https://doi.org/10.1007/s11042-017-4757-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4757-z

Keywords

Search

Navigation