Skip to main content
SpringerLink
Log in

A fuzzy histogram weighting method for efficient image contrast enhancement

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

Abstract

Image contrast enhancement is an important step in digital image processing applications. In this paper, we present an efficient contrast enhancement approach, which employs a histogram weighting method based on fuzzy system. It is able to enhance the contrast of input images while preserving their details. The proposed method divides the histogram of the original image into three sub-histograms using Fuzzy clustering. The obtained sub-histograms are weighted based on the Mamdani Fuzzy inference system, and then they are summed to generate a new histogram. The produced histogram is modified to reduce undesirable effects of its spikes and pits. Finally, the enhanced image is obtained by equalization of the modified histogram. The Mamdani fuzzy inference system assigns an appropriate dynamic range to each input interval of gray levels (sub-histogram), hence enhancing the image details. Experimental results for different types of images verified the merit of the proposed method in terms of preservation the input image details and improving its contrast.

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

Similar content being viewed by others

References

  1. Abdullah-Al-Wadud M, Kabir MH, Dewan MAA, Chae O (2007) A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(2):593–600

    Google Scholar 

  2. Arici T, Dikbas S, Altunbasak Y (2009) A histogram modification framework and its application for image contrast enhancement. IEEE Trans Image Process 18(9):1921–1935

    MathSciNet  MATH  Google Scholar 

  3. Beghdadi A, Le Negrate A (1989) Contrast enhancement technique based on local detection of edges. Computer Vision, Graphics, and Image Processing 46(2):162–174

    Google Scholar 

  4. Bereta M, Pedrycz W, Reformat M (2013) Local descriptors and similarity measures for frontal face recognition: a comparative analysis. J Vis Commun Image Represent 24(8):1213–1231

    Google Scholar 

  5. Casaca W, Boaventura M, de Almeida MP, Nonato LG (2014) Combining anisotropic diffusion, transport equation and texture synthesis for inpainting textured images. Pattern Recogn Lett 36:36–45

    Google Scholar 

  6. Celik T, Tjahjadi T (2012) Automatic image equalization and contrast enhancement using Gaussian mixture modeling. IEEE Trans Image Process 21(1):145–156

    MathSciNet  MATH  Google Scholar 

  7. Chen Z, Abidi BR, Page DL, Abidi MA (2006) Gray-level grouping (GLG): an automatic method for optimized image contrast Enhancement-part I: the basic method. IEEE Trans Image Process 15(8):2290–2302

    Google Scholar 

  8. Chen SD, Ramli AR (2003) Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans Consum Electron 49(4):1301–1309

    Google Scholar 

  9. Cheng F-C, Huang S-C (2013) Efficient histogram modification using bilateral Bezier curve for the contrast enhancement. J Disp Technol 9(1):44–50

    Google Scholar 

  10. Choukali MA, Valizadeh M, Amirani MC (2020) An efficient contrast enhancement method using repulsive force of edges. Multidim Syst Sign Process 31(1):299–315

    MathSciNet  MATH  Google Scholar 

  11. Eng H-L, Toh K-A, Yau W-Y, Wang J (2008) DEWS: a live visual surveillance system for early drowning detection at pool. IEEE Transactions on Circuits and Systems for Video Technology 18(2):196–210

    Google Scholar 

  12. Gao G, Wan X, Yao S, Cui Z, Zhou C, Sun X (2017) Reversible data hiding with contrast enhancement and tamper localization for medical images. Inf Sci 385:250–265

    Google Scholar 

  13. Gonzalez RC and Woods RE (2002). Thresholding, Digital Image Processing, 595–611

  14. Grima MA (2000) Neuro-fuzzy modelling in engineering geology. A.A.Balkema Publishers, Leiden

    Google Scholar 

  15. Hashemi S, Kiani S, Noroozi N, Moghaddam ME (2010) An image contrast enhancement method based on genetic algorithm. Pattern Recogn Lett 31(13):1816–1824

    Google Scholar 

  16. Hasikin, Khairunnisa, and Nor Ashidi Mat Isa (2012). Enhancement of the low contrast image using fuzzy set theory, In 2012 UKSim 14th International Conference on Computer Modelling and Simulation, 371–376

  17. He X, Chu WC, Yang H (2003) A new approach to verify rule-based systems using petri nets. Inf Softw Technol 45(10):663–669

    Google Scholar 

  18. Huang S-C, Chen W-C (2014) A new hardware-efficient algorithm and reconfigurable architecture for image contrast enhancement. IEEE Trans Image Process 23(10):4426–4437

    MathSciNet  MATH  Google Scholar 

  19. Huang S, Zhang Z, Zhao Y, Dai J, Chen C, Xu Y, Zhang E, Xie L (2014) 3D fingerprint imaging system based on full-field fringe projection profilometry. Opt Lasers Eng 52:123–130

    Google Scholar 

  20. İçen Dand Günay S (2019). Design and implementation of the fuzzy expert system in Monte Carlo methods for fuzzy linear regression, Appl Soft Comput, 77399–411

  21. Iqbal MZ, Ghafoor A, Siddiqui AM (2013) Satellite image resolution enhancement using dual-tree complex wavelet transform and nonlocal means. IEEE Geosci Remote Sens Lett 10(3):451–455

    Google Scholar 

  22. Jain AK (1989) Fundamentals of digital image processing. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  23. Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8

    Google Scholar 

  24. Li M, Xiao D, Zhang Y, Liu H (2014) Attack and improvement of the joint fingerprinting and decryption method for vector quantization images. Signal Process 99:17–28

    Google Scholar 

  25. Liao P-S, Chen T-S, Chung P-C (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 17(5):713–727

    Google Scholar 

  26. Liu K, Lewis FL (1993) Some issues about fuzzy logic control, In: Proceeding of 32nd IEEE Conference on Decision and Control, San Antonio, Texas, pp 1743–1748

  27. Magudeeswaran V, Ravichandran CG (2013) Fuzzy logic-based histogram equalization for image contrast enhancement. Math Probl Eng 2013:1–10

    MathSciNet  MATH  Google Scholar 

  28. Mahmood A, Khan SA, Hussain S, Almaghayreh EM (2019) An adaptive image contrast enhancement technique for low-contrast images. IEEE Access 7:161584–161593

    Google Scholar 

  29. Meshgini S, Aghagolzadeh A, Seyedarabi H (2013) Face recognition using Gabor-based direct linear discriminant analysis and support vector machine. Comput Electr Eng 39(3):727–745

    Google Scholar 

  30. Monjezi M, Rezaei MA, Varjani Y (2009) Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int J Rock Mech Min Sci 46(8):1273–1280

    Google Scholar 

  31. Muslim HSM, Khan SA, Hussain S, Jamal A, Qasim HSA (2019) A knowledge-based image enhancement and denoising approach. Computational and Mathematical Organization Theory 25(2):108–121

    Google Scholar 

  32. Olugu EU, Wong KY (2012) An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry. Expert Syst Appl 39(1):375–384

    Google Scholar 

  33. Ooi CH, Kong NSP, and Ibrahim H (2009). Bi-histogram equalization with a plateau limit for digital image enhancement, IEEE Trans Consum Electron, vol. 55, no. 4

  34. Otsu NA (1979) Threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Google Scholar 

  35. Panetta K, Agaian S, Zhou Y, Wharton EJ (2011) Parameterized logarithmic framework for image enhancement. IEEE Trans Syst Man Cybern B Cybern 41(2):460–473

    Google Scholar 

  36. Rafael CG, Woods R, Masters B (2009) Digital Image Processing Third Edition. J Biomed Opt 14(2):331–333

    Google Scholar 

  37. Riaz MM, Ghafoor A, Sreeram V (2013). Fuzzy C-means and principal component analysis based GPR image enhancement, In: 2013 IEEE radar conference (RADAR), 1–4

  38. Shanmugavadivu P, Balasubramanian K (2014) Thresholded and optimized histogram equalization for contrast enhancement of images. Comput Electr Eng 40(3):757–768

    Google Scholar 

  39. Shanmugavadivu P, Sumathy P, Vadivel A (2016) FOSIR: fuzzy-object-shape for image retrieval applications. Neurocomputing 171:719–735

    Google Scholar 

  40. Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    MathSciNet  MATH  Google Scholar 

  41. Sim K, Tso C, Tan Y (2007) Recursive sub-image histogram equalization applied to gray scale images. Pattern Recogn Lett 28(10):1209–1221

    Google Scholar 

  42. Singh K, Kapoor R (2014) Image enhancement using exposure based sub image histogram equalization. Pattern Recogn Lett 36:10–14

    Google Scholar 

  43. Sulochana S, Vidhya R (2011) Satellite image contrast enhancement using multiwavelets and singular value decomposition (SVD). Int J Comput Appl 35(7):1–5

    Google Scholar 

  44. Takagi, T, and Sugeno, M (1993). Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst, 116–132

  45. Tang JR, Isa NAM (2014) Adaptive image enhancement based on bi-histogram equalization with a clipping limit. Comput Electr Eng 40(8):86–103

    Google Scholar 

  46. Tang J, Liu X, Sun Q (2009) A direct image contrast enhancement algorithm in the wavelet domain for screening mammograms. IEEE Journal of Selected Topics in Signal Processing 3(1):74–80

    Google Scholar 

  47. Tsai C-M (2013) Adaptive local power-law transformation for color image enhancement. Applied Mathematics & Information Sciences 7(5):2019

    MathSciNet  Google Scholar 

  48. Vadivel A, Shaila SG (2016) Event pattern analysis and prediction at sentence level using Neuro-fuzzy model for crime event detection. Pattern Anal Applic 19(3):679–698

    MathSciNet  Google Scholar 

  49. Vadivel A, Sural S, Majumdar AK (2005) Human color perception in the HSV space and its application in histogram generation for image retrieval. In Color Imaging X: Processing, Hardcopy, and Applications. International Society for Optics and Photonics 5667:598–609

    Google Scholar 

  50. Wang X, Chen L (2017) An effective histogram modification scheme for image contrast enhancement. Signal Process Image Commun 58:187–198

    Google Scholar 

  51. Wang Y, Chen Q, Zhang B (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans Consum Electron 45(1):68–75

    Google Scholar 

  52. Yang X, Shen X, Long J, Chen H (2012) An improved medianbasedOtsu image thresholding algorithm. AASRI Procedia 3:468–473

    Google Scholar 

  53. Zadeh, LA, Fu, KS, Tanaka, K and Shimura, M (1975). Calculus of fuzzy restriction, Fuzzy sets and their applications to cognitive and decision processing, 1–40

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran

    Sedighe Mirbolouk, Morteza Valizadeh, Mehdi Chehel Amirani & Mohammad Amin Choukali

Authors
  1. Sedighe Mirbolouk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Morteza Valizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mehdi Chehel Amirani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad Amin Choukali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Morteza Valizadeh.

Additional information

Publisher’s note

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

Appendix

Appendix

1.1 Verification of proposed fuzzy if-then rules based on ω-net

Generally, to verify rule-based systems, it is necessary to investigate different kinds of errors like: inconsistency (conflict rules), incompleteness (missing rules), redundancy (redundant rules), and circularity (circular depending rules) [17, 48]. For verification of fuzzy rules, a special kind of Petri nets named ω-nets (for more details refer to [17]) was adopted. Considering new notation for the antecedent and conclusion parts of the presented rules in section 3.2 as p1: Nj is mf1, p2: Nj is mf2, p3: Nj is mf3, p4: Sj is mf1, p5: Sj is mf2, p6: Sj is mf3, p7: W is M1, p8: W is M2, p9: W is M3, p10: W is M4, p11: W is M5, p12: W is M6, p13: W is M7, such rules can be rewritten as below rule base:

$$ {\displaystyle \begin{array}{l}R=\Big\{r1:p1\wedge p4\to p7,\kern0.75em r2:p1\wedge p5\to p9,\kern0.75em r3:p1\wedge p6\to p11,\kern0.75em r4:p2\wedge p4\to p8,\\ {}r5:p2\wedge p5\to p10,\kern0.75em r6:p2\wedge p6\to p12,\kern0.75em r7:p3\wedge p4\to p9,\kern0.75em r8:p3\wedge p5\to p11,\\ {}r9:p3\wedge p6\to p13\Big\}\end{array}} $$

The corresponding ω-net is constructed as shown in Fig. 11.

Fig. 11
figure 11

The ω-net for rule base R

Full size image

Finally, by investigating the reachability graph [17] from above ω-net with node vector (p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13), drawn in Fig. 12, the following results can be extracted:

  1. 1.

    There are no missing rules, i.e. no incompleteness errors due to the existence of all the places (p).

  2. 2.

    The reachability graph has no contradictory places, therefore no inconsistency errors.

  3. 3.

    There is no circularity because of the lack of any loop in the reachability graph.

  4. 4.

    Since there are no useless duplicated rules, the rule base is free from redundancy.

Fig. 12
figure 12

The reachability graph of R

Full size image

Accordingly, it is found that there are no anomalies in the proposed fuzzy rules in this paper, thus, they are verified.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mirbolouk, S., Valizadeh, M., Amirani, M.C. et al. A fuzzy histogram weighting method for efficient image contrast enhancement. Multimed Tools Appl 80, 2221–2241 (2021). https://doi.org/10.1007/s11042-020-09801-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09801-w

Keywords

Search

Navigation