Skip to main content
SpringerLink
Log in

Low-Illumination Color Image Enhancement Using Intuitionistic Fuzzy Sets

  • Conference paper
  • First Online:
Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 891))

  • 710 Accesses

Abstract

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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). https://doi.org/10.1109/iske.2017.8258819

  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). https://doi.org/10.1109/icce-china.2017.7991117

  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). https://doi.org/10.1109/ipact.2017.8244991

  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). https://doi.org/10.1016/j.cviu.2006.02.007

    Article  Google Scholar 

  5. Pal, S.K., King, R.A.: Image enhancement using smoothing with fuzzy sets. IEEE Trans. Syst. Man Cybern. 11(7), 494–501 (1981). https://doi.org/10.1109/tsmc.1981.4308726

    Article  Google Scholar 

  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). https://doi.org/10.1016/j,patrec.2004.06.006

    Article  Google Scholar 

  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). https://doi.org/10.1016/j,ijar.2006.10.002

    Article  MATH  Google Scholar 

  8. Chaira, T.: Intuitionistic fuzzy segmentation of medical images. IEEE Trans. Bio. Eng. 57(6), 1430–1436 (2010). https://doi.org/10.1109/tbme.2010.2041000

    Article  Google Scholar 

  9. Atanassov, K.T.: Intuitionistic fuzzy set. Fuzzy Set. Syst. 20(1), 87–96 (1986). https://doi.org/10.1016/s0165-0114(86)80034-3

    Article  MathSciNet  Google Scholar 

  10. Bustunce, H.: Restricted equivalence functions. Fuzzy Sets Syst. 157(17), 2333–2346 (2006). https://doi.org/10.1016/j.fss.2006.03.018

    Article  MathSciNet  Google Scholar 

  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). https://doi.org/10.1016/j.patcog.2014.07.003

    Article  Google Scholar 

  12. Wang, S., Chung, F., Xiong, F.: A novel image thresholding method based on Parzen window estimate. Pattern Recogn. 41(1), 117–129 (2008). https://doi.org/10.1016/j.patcog.2007.03.029

    Article  MATH  Google Scholar 

  13. Deng, H., Deng, W., Sun, X., et al.: Mammogram enhancement using intuitionistic fuzzy sets. IEEE Trans. Biomed. Eng. PP(99), 1 (2016). https://doi.org/10.1109/tbme.2016.2624306

    Article  Google Scholar 

  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). https://doi.org/10.1109/tsmcb.2010.2058847

    Article  Google Scholar 

  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). https://doi.org/10.1109/titb.2011.2164259

    Article  Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

  1. Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Xiumei Cai, Jinlu Ma, Chengmao Wu & Yongbo Ma

Authors
  1. Xiumei Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinlu Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengmao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongbo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinlu Ma .

Editor information

Editors and Affiliations

  1. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Pavel Krömer

  2. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, China

    Hong Zhang

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cai, X., Ma, J., Wu, C., Ma, Y. (2019). Low-Illumination Color Image Enhancement Using Intuitionistic Fuzzy Sets. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_22

Download citation

Publish with us

Policies and ethics

Search

Navigation