Improved Fuzzy Image Enhancement Using L*a*b* Color Space and Edge Preservation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 384)


Image enhancement is a process of improving the perceptibility of an image so that the output image is better than input image. The traditional image enhancement techniques may affect the edges of an image which leads to loss of perceptual information. The existing techniques use primary/secondary color spaces which are device-dependent. This research paper works on these two issues. It uses L*a*b* color space which is device independent. To evaluate fuzzy membership values, L component is stretched while preserving the chromatic information a and b. Moreover, an edge preserving smoothing has been integrated with fuzzy image enhancement so that edges are not affected and remain preserved. The proposed technique is compared with existing techniques such as Histogram equalization, Adaptive histogram equalization and fuzzy based enhancement. The experimental results indicate that the proposed technique outperforms the existing techniques.


Image enhancement Fuzzy-logic L*a*b* color space (CIELAB) Color images Edge preservation, histogram equalization 


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  1. 1.
    Hanmandlu, M., Jha, D.: An Optimal Fuzzy System for Color Image Enhancement. IEEE Transactions on Image Processing 2956–2966 (2006)Google Scholar
  2. 2.
    Raju, G., Nair, M.S.: A fast and efficient color image enhancement method based on fuzzy-logic and histogram. AEU- International Journal of Electronics and Communications 237–243 (2014)Google Scholar
  3. 3.
    Gao, M.-Z., Wu, Z.-G., Wang, L.: Comprehensive evaluation for HE based contrast enhancement techniques. In: Advances in Intelligent Systems and Applications, vol. 2, pp. 331–338. Springer, Heidelberg (2013)Google Scholar
  4. 4.
    Celik, T.: Spatial Entropy-Based Global and Local Image contrast Enhancement. IEEE Transactions on Image Processing 5298–5308 (2014)Google Scholar
  5. 5.
    bt. Shamsuddin, N., bt. Wan Ahmad, W.F., Baharudin, B.B., Kushairi, M., Rajuddin, M., bt. Mohd, F.: Significance level of image enhancement techniques for underwater images. In: 2012 International Conference on Computer & Information Science (ICCIS), pp. 490–494 (2012)Google Scholar
  6. 6.
    Senthilkumaran, N., Thimmiaraja, J.: Histogram equalization for image enhancement using MRI brain images. In: 2014 World Congress on Computing and Communication Technologies (WCCCT), pp. 80–83. IEEE (2014)Google Scholar
  7. 7.
    Kaur, M., Kaur, J., Kaur, J.: Survey of contrast enhancement techniques based on histogram equalization. International Journal of Advanced Computer Science and Applications (2011)Google Scholar
  8. 8.
    Kim, B., Gim, G.Y., Park, H.J.: Dynamic histogram equalization based on gray level labeling. IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics (2014)Google Scholar
  9. 9.
    Cheng, D., Shi, D., Tang, X., Liu, J.: A local-context-based fuzzy algorithm for image enhancement. In: Yang, J., Fang, F., Sun, C. (eds.) IScIDE 2012. LNCS, vol. 7751, pp. 165–171. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Hassanien, A.E., Soliman, O.S., El-Bendary, N.: Contrast enhancement of breast MRI images based on fuzzy type-II. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds.) SOCO 2011. AISC, vol. 87, pp. 77–83. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Sudhavani, G., et al.: K enhancement of low contrast images using fuzzy techniques. In: 2015 International Conference on Signal Processing and Communication Engineering Systems (SPACES). IEEE, pp. 286–290 (2015)Google Scholar
  12. 12.
    Tehranipour, F., et al.: Attention control using fuzzy inference system in monitoring CCTV based on crowd density estimation. In: 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), pp. 204–209. IEEE (2013)Google Scholar
  13. 13.
    Nair, M.S., et al.: Fuzzy logic-based automatic contrast enhancement of satellite images of ocean. Signal, Image and Video Processing 5(1), 69–80 (2011)MATHCrossRefGoogle Scholar
  14. 14.
    Hasikin, K., Isa, N.A.M.: Fuzzy image enhancement for low contrast and non-uniform illumination images. In: IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 275–280. IEEE (2013)Google Scholar
  15. 15.
    Liejun, W., Ting, Y.: A new approach of image enhancement based on improved fuzzy domain algorithm. In: International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), pp. 1–5. IEEE (2014)Google Scholar
  16. 16.
    Alajarmeh, A., et al.: Real-time video enhancement for various weather conditions using dark channel and fuzzy logic. In: 2014 International Conference on Computer and Information Sciences (ICCOINS), pp. 1–6. IEEE (2014)Google Scholar
  17. 17.
    Aggarwal, A., Garg, A.: Medical image enhancement using Adaptive Multiscale Product Thresholding. In: 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), pp. 683–687. IEEE (2014)Google Scholar
  18. 18.
    Kotkar, V.A., Gharde, S.S.: Image contrast enhancement by preserving brightness using global and local features, pp. 262–271 (2013)Google Scholar
  19. 19.
    Humied, I.A., Abou-Chadi, F.E.Z., Rashad, M.Z.: A new combined technique for automatic contrast enhancement of digital images. Egyptian Informatics Journal 27–37 (2012)Google Scholar
  20. 20.
    Ganesan, P., Rajini, V., Rajkumar, R.I.: Segmentation and edge detection of color images using CIELAB color space and edge detectors. In: 2010 International Conference on Emerging Trends in Robotics and Communication Technologies (INTERACT), pp. 393–397. IEEE (2010)Google Scholar
  21. 21.
    Ramadan, Z.M.: A New Method for Impulse Noise Elimination and Edge Preservation. Canadian Journal of Electrical and Computer Engineering 2–10 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceDAV UniversityJalandharIndia

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