Triangular Fuzzy Membership-Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) for Enhancement of Multimodal Biometric Images

  • B. Sree VidyaEmail author
  • E. Chandra


The research work proposes a novel triangular fuzzy membership (TFM) function-based contrast limited adaptive histogram equalization (CLAHE) for biometric image enhancement. Biometric images have wide applications in the areas of verification and authentication systems. For accurate identification and verification, pre-processing of captured biometric images becomes essential. When the region of interest is smaller than the original image, a variation of histogram equalization called adaptive histogram equalization (AHE) is used. AHE enhances contrast of images by considering local regions. Along with local contrast, noise in those regions also get amplified by using AHE. This amplification of noise can be resolved by applying a contrast limited AHE (CLAHE) which limits the contrast in the enhanced local regions by clipping the histogram at a pre-fixed limit. CLAHE yields good results by limiting the contrast and enhancing local regions, but it is image invariant since it uses pre-determined clip limit for limiting contrast. The proposed research work TFM-CLAHE puts forward the idea of image variant, automatic clip value determinant algorithm for enhancement. The algorithm employs triangular fuzzy membership function to determine clip-limit and limits contrast by clipping the histogram at the computed clip-level. TFM function computes the clipping parameter by considering intensities of pixels. The computed fuzzy clip-limit overrides the pre-defined limit. Consequently, the clipping parameter varies according to the image under consideration and yields better enhancement results. The proposed work is experimented on multimodal biometric images acquired from Chinese Academy of Science, Institute of Automation Iris, Face and Fingerprint databases. TFM-CLAHE computes appropriate clipping limit for each of these heterogenous images. The results of the proposed work are evaluated on the grounds of images’ average information content, mean square error, peak signal noise ratio, natural image quality evaluator, no-reference free energy based robust metric, blind image quality measure of enhanced images and no reference quality metric for contrast distortion. The results show good enhancement and these are compared with existing conventional image enhancement techniques.


Adaptive histogram equalization (AHE) Biometric images Contrast limited adaptive histogram equalization (CLAHE) Triangular fuzzy membership-contrast limited adaptive histogram equalization (TFM-CLAHE) Triangular fuzzy membership (TFM) Multimodal biometric images 


Compliance with ethical standards

Conflict of interest

The authors declare that there are no competing interests.


  1. 1.
    Pratt, W. K. (2007). Digital image processing: PIKS inside (4th ed.). New York: Wiley.zbMATHGoogle Scholar
  2. 2.
    Su, X., Fang, W., Shen, Q., & Hao, X. (2013). An image enhancement method using the quantum-behaved particle swarm optimization with an adaptive strategy. Mathematical Problems in Engineering, 2013, 3.MathSciNetGoogle Scholar
  3. 3.
    Gu, K., Zhai, G., & Yang, X. (2014). Automatic contrast enhancement technology with saliency preservation. IEEE Transactions on Circuits and Systems for Video Technology, 25, 1480–1494.Google Scholar
  4. 4.
    Ravichandran, C. G., & Magudeeswaran, V. (2012). An efficient method for contrast enhancement in still images using histogram modification framework. Journal of Computer Science, 8(5), 775–779.Google Scholar
  5. 5.
    Kim, Y. (1997). Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics, 43(1), 1–8.Google Scholar
  6. 6.
    Wan, Y., Chen, Q., & Zhang, B.-M. (1999). Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Transactions on Consumer Electronics, 45(1), 68–75.Google Scholar
  7. 7.
    Chen, S. D., & Ramli, A. R. (2003). Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Transactions on Consumer Electronics, 49(4), 1310–1319.Google Scholar
  8. 8.
    Chen, S. D., & Ramli, A. R. (2003). Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Transactions on Consumer Electronics, 49(4), 1301–1309.Google Scholar
  9. 9.
    Abdullah-Al-Wadud, M., Kabir, M. H., Dewan, M. A. A., & Chae, O. (2007). A dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(2), 593–600.Google Scholar
  10. 10.
    Kong, N. S. P., & Ibrahim, H. (2008). Color image enhancement using brightness preserving dynamic histogram equalization. IEEE Transactions on Consumer Electronics, 54(4), 1.Google Scholar
  11. 11.
    Magudeeswaran, V., & Fensia Singh, J. (2017). Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images. International Journal of Imaging Systems and Technology, 27, 98–103.Google Scholar
  12. 12.
    Chandra, E., & Kanagalakshmi, K. (2011). Noise elimination in fingerprint image using median filter. International Journal of Advanced Networking and Application, 2(6), 950–955.Google Scholar
  13. 13.
    Maurya, L., Mahapatra, P. K., & Kumar, A. (2017). A social spider image fusion approach for contrast enhancement and brightness preservation. Applied Soft Computing, 52, 575–592.Google Scholar
  14. 14.
    Jenifer, S., Parasuraman, S., & Kadirvelu, A. (2016). Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast limited adaptive histogram equalization algorithm. Applied Soft Computing, 42, 167.Google Scholar
  15. 15.
    Magudeeswaran, V., & Ravichandran, V. (2013). Fuzzy logic based histogram equalization for image contrast enhancement. Mathematical Problems in Engineering, 2013, 1.MathSciNetzbMATHGoogle Scholar
  16. 16.
    Gui, Z., & Liu, Y. (2011). An image sharpening algorithm based on fuzzy logic. Optik, 122, 697–702.Google Scholar
  17. 17.
    Majumdar, J., & Kumar, S. (2014). Modified CLAHE: an adaptive algorithm for contrast enhancement of arial, medical and underwater images. International Journal of Computer Engineering and Technology, 5(11), 32–47.Google Scholar
  18. 18.
    Wei, Z., Lidong, H., Jun, W., & Zebin, S. (2015). Entropy maximization histogram modification scheme for image enhancement. IET Image Processing, 9, 226–235.Google Scholar
  19. 19.
    Tang, J. R., & Mat Isa, N. A. (2014). Adaptive image enhancement based on bi-histogram equalization with a clipping limit. Computers and Electrical Engineering, 40(8), 86–103.Google Scholar
  20. 20.
    Zhuang, L., & Guan, Y. (2017). Image enhancement via subimage histogram equalization based on mean and variance. Computational Engineering and Neuroscience, 2017, 1.Google Scholar
  21. 21.
    Saxena, K., Pokhriyal, A., & Lehri, S. (2014). SCIENCE: Soft computing image enhancement for contrast enhancement. International Journal of Advanced Computing, 47(1), 1.Google Scholar
  22. 22.
    Sree Vidya, B., & Pugazhenthi, D. (2013). Multiple biometric security in cloud computing. International Journal of Advanced Research in Computer Science and Engineering, 3(4), 1.Google Scholar
  23. 23.
    Sree Vidya, B., & Pugazhenthi, D. (2015). Multimodal biometric cryptographic based in cloud environment to enhance information security. International Conference World Academy of Science Engineering and Technology, 2, 1.Google Scholar
  24. 24.
    Wang, Z., & Tao, J. (2006). A fast implementation of adaptive histogram equalization. In 8th international conference on signal processing (Vol. 2).Google Scholar
  25. 25.
    Hossain, F., & Alsharif, M. R. (2007). Image enhancement based on logarithmic transform coefficient and adaptive histogram equalization. International Conference on Convergence Information Technology, 1, 1439–1444.Google Scholar
  26. 26.
    Reza, A. M. (2004). Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, 38(1), 35–44.Google Scholar
  27. 27.
    Hitam, M. S., Awalludin, E. A., Yussof, W. N. J. H. W., & Bachok, Z. (2013). Mixture contrast limited adaptive histogram equalization for underwater image enhancement. In International conference on computer applications technology (ICCAT) (pp. 1–5).Google Scholar
  28. 28.
    Ooi, C. H., Pikkong, N. S., & Ibrahim, H. (2009). Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Transactions on Consumer Electronics, 55(4), 2072–2080.Google Scholar
  29. 29.
    Liang, K., Ma, Y., Xie, Y., Zhou, B., & Wang, R. (2012). A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization. Infrared Physics and Technology, 55, 309–315.Google Scholar
  30. 30.
    Chang, Y., & Chang, C. (2010). A simple histogram modification scheme for contrast enhancement. IEEE Transactions on Consumer Electronics, 56(2), 737–742.Google Scholar
  31. 31.
    Moorthy, A. K., & Bovik, A. C. (2011). Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 20(12), 3350–3364.MathSciNetzbMATHGoogle Scholar
  32. 32.
    Mittal, A., Soundararajan, R., & Bovik, A. C. (2013). Making a completely blind image quality analyzer. IEEE Signal Processing Letters, 20(3), 209–212.Google Scholar
  33. 33.
    Sree Vidya, B., & Chandra, E. (2018). Multimodal biometric hashkey cryptography based authentication and encryption for advanced security in cloud. Biomedical Research, 5, 506–516.Google Scholar
  34. 34.
    Gu, K., Lin, W., & Zhai, G. (2016). No-reference quality metric of contrast-distorted images based on information maximization. IEEE Transactions on Cybernatics, 47, 4559.Google Scholar
  35. 35.
    Gu, K., & Tao, D. (2017). Learning a no-reference quality assessment model of enhanced images with big data. IEEE Transactions on Neural Networks and Learning Systems, 29, 1301.Google Scholar
  36. 36.
    Gu, K., Zhai, G., Yang, X., & Zhang, W. (2015). Using free energy principle for blind image quality assessment. IEEE Transactions on Multimedia, 17(1), 50–63.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Bharathiyar UniversityCoimbatoreIndia
  2. 2.Department of Computer ScienceBharathiyar UniversityCoimbatoreIndia

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