Low-Illumination Color Image Enhancement Using Intuitionistic Fuzzy Sets

  • Xiumei Cai
  • Jinlu MaEmail author
  • Chengmao Wu
  • Yongbo Ma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


Because low illumination color image has the features of low brightness, poor contrast and dark color, and the enhancement effect of traditional image enhancement algorithm is very limited. A low illumination image enhancement algorithm based on fuzzy set theory is proposed, by transformed the RGB image into HSV space, and the brightness component V of the image is used to enhance the image in fuzzy plane. The experimental results show that this method is better than the traditional enhancement according to fuzzy set and the operation efficiency is higher, which can realize the clearness processing of low illumination image effectively.


Intuitionistic fuzzy sets Low illumination image Contrast enhancement 



This work was supported by the Department of Education Shaanxi Province (16JK1712), Shaanxi Provincial Natural Science Foundation of China (2016JM8034, 2017JM6107), and the National Natural Science Foundation of China (61671377, 51709228).


  1. 1.
    Du, Y., Wu, G., Tang, G.: Auto-encoder based clustering algorithms for intuitionistic fuzzy sets. In: International Conference on Intelligent Systems and Knowledge Engineering, pp. 1–6 (2017).
  2. 2.
    Lee, S.L., Tseng, C.C.: Color image enhancement using histogram equalization method without changing hue and saturation. In: IEEE International Conference on Consumer Electronics – Taiwan, pp. 305–306. IEEE (2017).
  3. 3.
    Bhairannawar, S., Patil, A., Janmane, A., et al.: Color image enhancement using Laplacian filter and contrast limited adaptive histogram equalization. In: IEEE International Conference on Innovations in Power and Advanced Computing Technologies, vol. 8(27), pp. 32–34 (2018).
  4. 4.
    Huang, K., Wang, Q., Wu, Z.: Natural color image enhancement and evaluation algorithm based on human visual system. Comput. Vis. Image Underst. 103(1), 52–63 (2006). Scholar
  5. 5.
    Pal, S.K., King, R.A.: Image enhancement using smoothing with fuzzy sets. IEEE Trans. Syst. Man Cybern. 11(7), 494–501 (1981). Scholar
  6. 6.
    Hung, W.L., Yang, M.S.: Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recogn. Lett. 25(14), 1603–1611 (2004).,patrec.2004.06.006CrossRefGoogle Scholar
  7. 7.
    Hung, W.L., Yang, M.S.: Similarity measures of intuitionistic fuzzy sets based on LP metric. Int. J. Approximate Reasoning 46(1), 120–136 (2007).,ijar.2006.10.002CrossRefzbMATHGoogle Scholar
  8. 8.
    Chaira, T.: Intuitionistic fuzzy segmentation of medical images. IEEE Trans. Bio. Eng. 57(6), 1430–1436 (2010). Scholar
  9. 9.
    Atanassov, K.T.: Intuitionistic fuzzy set. Fuzzy Set. Syst. 20(1), 87–96 (1986). Scholar
  10. 10.
    Bustunce, H.: Restricted equivalence functions. Fuzzy Sets Syst. 157(17), 2333–2346 (2006). Scholar
  11. 11.
    Ananthi, V.P., Balasubramaniam, P., Lim, C.P.: Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions. Pattern Recogn. 47(12), 3870–3880 (2014). Scholar
  12. 12.
    Wang, S., Chung, F., Xiong, F.: A novel image thresholding method based on Parzen window estimate. Pattern Recogn. 41(1), 117–129 (2008). Scholar
  13. 13.
    Deng, H., Deng, W., Sun, X., et al.: Mammogram enhancement using intuitionistic fuzzy sets. IEEE Trans. Biomed. Eng. PP(99), 1 (2016). Scholar
  14. 14.
    Panetta, K., Agaian, S., Zhou, Y., et al.: Parameterized logarithmic framework for image enhancement. IEEE Trans. Syst. Man Cybern. Part B Cybern. Publ. IEEE Syst. Man Cybern. Soc. 41(2), 460–473 (2011). Scholar
  15. 15.
    Panetta, K., Zhou, Y., Agaian, S., et al.: Nonlinear unsharp masking for mammogram enhancement. IEEE Trans. Inf. Technol. Biomed. Publ. IEEE Eng. Med. Biol. Soc. 15(6), 918–928 (2011). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Xi’an University of Posts and TelecommunicationsXi’anChina

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