Skip to main content
SpringerLink
Log in

A new fuzzy rule based pixel organization scheme for optimal edge detection and impulse noise removal

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

Abstract

Fuzzy sets provide a framework for incorporating human knowledge as an efficient unsupervised machine learning tool for problem solving. The approach discussed in this paper introduces a generalized transfer learning scheme using rule based fuzzy logic for edge detection in digital images. The spatial domain statistical properties of the image are explored as training data set and expressed in fuzzy format to obtain a decision function for optimal edge detection along with reduction of impulse noise. During fuzzy inference process, a specific linguistic value in input fuzzy set is selected in order to obtain an optimal range of second order difference which discriminates the edge pixels from the non-edge pixels. The proposed fuzzy rule based optimal edge pixel detection method in the presence of random valued impulse noise tends to sufficiently extract the edge pixels with out boosting the noisy pixels. The effectiveness of the proposed fuzzy rule based edge detection scheme is verified by testing it on various standard test images and comparing with existing edge detection techniques at different noise densities.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Andrews HC, Patterson CL (1976) Singular value decompositions and digital image processing, IEEE transactions on acoustics, speech and signal processing, ASSP-24, 26–53

  2. Barrenechea E, Bustince H, Baets BD, Molina CL (2011) Construction of interval-valued fuzzy relations with application to the generation of fuzzy edge images. IEEE Trans Fuzzy Syst 19(5):819–830

    Article  Google Scholar 

  3. Basu M (2002) Gaussian-based edge-detection methods: a survey. IEEE Trans Syst, Man Cybern Part C - Appl Rev 32(3):252–260

    Article  Google Scholar 

  4. Bezdek JC, Chadrashekhar R, Attikiouzel Y (1998) A geometric approach to edge detection. IEEE Trans Fuzzy Syst 6(1):52–75

    Article  Google Scholar 

  5. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI-8 6:679–698

    Article  Google Scholar 

  6. Carnicer RM, Cuevas FJM, Garcia NLF, Poyato AC (2005) Evaluation of global thresolding techniques in non-contextual edge detection. Pattern Recogn Lett 26:1423–1434

    Article  Google Scholar 

  7. Carnicer RM, Poyato AC, Salinas RM, Cuevas FJM (2010) Determining hysteresis thresholds for edge detection by combining the advantages and disadvantages of thresholding methods. IEEE Trans Image Process 19(1):165–173

    Article  MathSciNet  MATH  Google Scholar 

  8. Caruana R (1997) Multitask learning. Mach Learn 28(1):41–75

    Article  MathSciNet  Google Scholar 

  9. Catte F, Lions PL, Morel JM, Coll T (1992) Image selective smoothing and edge-detection by nonlinear diffusion. SIAM J Numer Anal 29:182–193

    Article  MathSciNet  MATH  Google Scholar 

  10. Chan FHY, Lam FK, Zhu H (1998) Adaptive thresholding by variational method. IEEE Trans Image Process 7(3):468–473

    Article  Google Scholar 

  11. Chao LT (2007) A new adaptive center weighted median filter for suppressing impusive noise in images. Inf Sci 177:1073–1087

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. El-Khamy SE, Ghaleb I, El-Yamany NA (2002) Fuzzy Edge Detection With Minimum Fuzzy Entropy Criterion, IEEE Mediterranean Electrotechnical Conference, Cairo, Egypt

  14. Elder JH, Zucker SW (1998) Local scale control for edge detection and blur estimation. IEEE Trans Pattern Anal Mach Intell 20(7):699–717

    Article  Google Scholar 

  15. Gonzalez RC, Woods RE (2008) Digital image processing, Prentice Hall Chapter 3, 144–168

  16. Javed U, Riaz MH, Ghafoor A, Cheema TA (2016) SAR image segmentation based on active contours with fuzzy logic. IEEE Trans Aerosp Electron Syst 52(1):181–189

    Article  Google Scholar 

  17. Khan NU, Arya KV, Pattanaik M (2014) Edge preservation of impulse noise filtered images by improved anisotropic diffusion. Multimed Tool Appl 73:573–597

    Article  Google Scholar 

  18. Li J, Lu K, Huang Z, Zhu L, Shen HT (2019) Transfer independently together: a generalized framework for domain adaptation. IEEE Trans Cybern 49 (6):2144–2155

    Article  Google Scholar 

  19. Mafi M, Rajaei H, Cabrerizo M, Adjouadi M (2018) A robust edge detection approach in the presence of high impulse noise intensity through switching adaptive median and fixed weighted mean filtering. IEEE Trans Image Process 27(11):5475–5491

    Article  MathSciNet  Google Scholar 

  20. Melin P, Gonzalez CI, Castro JR, Mendoza O, Castillo O (2014) Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans Fuzzy Syst 22(6):1515–1526

    Article  Google Scholar 

  21. Molina CL, Baets BD, Bustince H (2011) Generating fuzzy edge images from gradient magnitudes. Comput Vis Image Underst 115:1571–1580

    Article  Google Scholar 

  22. Nadernejad E, Korhonen J, Forchhammer S, Burini N (2012) Enhancing perceived quality of compressed images and video with anisotropic diffusion and fuzzy filtering. Signal Process: Image Commun 28(3):222–240

    Google Scholar 

  23. Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern SMC-8:62–66

    Article  Google Scholar 

  24. Pagola M, Molina CL, Fernandez J, Barrenechea E, Bustince H (2013) Interval type-2 fuzzy sets constructed from several membership functions: application to the fuzzy thresholding algorithm. IEEE Trans Fuzzy Syst 21(2):230–245

    Article  Google Scholar 

  25. Pal SK, King RA (1981) Image enhancement using smoothing with fuzzy sets. IEEE Trans Syst, Man Cybern SMC-11 7:494–501

    Google Scholar 

  26. Perona P, Malik J (1990) Scale space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(8):629–639

    Article  Google Scholar 

  27. Pinho AJ, Almeida AB (1996) Figures of merit for quality assessment of binary edge maps. In: Proceedings of 3rd IEEE International Conference on Image Processing (ICIP 96), Lausanne, Swittzerland, September 16-19, vol 3, pp 591–594

  28. Polesel A, Ramponi G, Mathews VJ (2000) Image enhancement via adaptive unsharp masking. IEEE Trans Image Process 9(3):505–511

    Article  Google Scholar 

  29. Pratondo A, Chui CK, Ong SH (2016) Robust edge stop functions for edge - based active contour models in medical image segmentation. IEEE Signal Process Lett 23(2):222–227

    Article  Google Scholar 

  30. Rosin PL (2001) Unimodal thresholding. Pattern Recogn 34:2083–2096

    Article  MATH  Google Scholar 

  31. Ross TJ (2009) Fuzzy Logic with Engineering Applications, Wiley

  32. Russo F (1998) Edge detection in noisy images using fuzzy reasoning, IEEE instrumentation and measurement technology conference, St.Paul, minnesota, USA, 18–21

  33. Shell J, Coupland S (2015) Fuzzy tranfer learning: methodology and application. Inform Sci 293:59–79

    Article  Google Scholar 

  34. Sujamol S, Ashok S, Krishna Kumar U (2017) Fuzzy based machine learning: a promising approach. CSI Commun 41(8):21–24

    Google Scholar 

  35. Sun G, Liu Q, Liu Q, Ji C, Li X (2007) A novel approach for edge detection based on the theory of universal gravity. Pattern Recogn 40:2766–2775

    Article  MATH  Google Scholar 

  36. Verma OP, Parihar AS (2017) An optimal fuzzy system for edge detection in color images using bacterial foraging algorithm. IEEE Trans Fuzzy Syst 25(1):114–128

    Article  Google Scholar 

  37. Wang H, Fei B (2009) A modified fuzzy C-means classification method using a multiscale diffusion filtering scheme. Med Image Anal 13(2):193–202

    Article  MathSciNet  Google Scholar 

  38. Wu J, Yin Z, Xiong Y (2009) The fast multilevel fuzzy edge detection of blurry images. IEEE Signal Process Lett 14(5):344–347

    Article  Google Scholar 

  39. You Y, Kaveh M (2000) Fourth-order partial differential equations for noise removal. IEEE Trans Image Process 9(10):1723–1730

    Article  MathSciNet  MATH  Google Scholar 

  40. Zhang B, Allebach JP (2008) Adaptive bilateral filter for sharpness enhancement and noise removal. IEEE Trans Image Process 17(5):664–678

    Article  MathSciNet  Google Scholar 

  41. Zhao M, Fu AMN, Yan H (2001) A technique of three level thresholding based on probability partition and fuzzy 3 - partition. IEEE Trans Fuzzy Syst 9(3):469–480

    Article  Google Scholar 

  42. Zimmermann H (2001) Fuzzy set theory and its applications. Springer, Berlin

    Book  Google Scholar 

  43. Ziolko B, Emms D, Ziolko M (2018) Fuzzy evaluations of image segmentations. IEEE Trans Fuzzy Syst 26(4):1789–1799

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, 173234, India

    Nafis uddin Khan

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

    K. V. Arya

Authors
  1. Nafis uddin Khan
    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 Nafis uddin Khan.

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

Khan, N.u., Arya, K.V. A new fuzzy rule based pixel organization scheme for optimal edge detection and impulse noise removal. Multimed Tools Appl 79, 33811–33837 (2020). https://doi.org/10.1007/s11042-020-08707-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08707-x

Keywords

Search

Navigation