Grayscale Image Enhancement Using Improved Cuckoo Search Algorithm

  • Samiksha AroraEmail author
  • Prabhpreet Kaur
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 518)


Meta-heuristic algorithms have been proved to play a significant role in the automatic image enhancement domain which can be regarded as an optimization question. Cuckoo search algorithm is one such algorithm which uses Levy flight distribution to find out the optimized parameters affecting the enhanced image. In this paper, improved cuckoo search algorithm is proposed which is used to achieve the better optimized results. The proposed method is implemented on some test images, and results are compared with original cuckoo search algorithm.


Image enhancement Cuckoo search Meta-heuristics Gauss distribution Improved cuckoo search algorithm 


  1. 1.
    Gonzalez, R.C., Woods, R.E.: Digital image processing. ed: Prentice Hall Press, ISBN 0-201-18075-8 (2002).Google Scholar
  2. 2.
    Pratt, William K.: Digital Image Processing. PIKS Scientific Inside, Fourth Edition. p. 651–678, (1991).Google Scholar
  3. 3.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using MATLAB. 2nd ed: Prentice Hall Press.Google Scholar
  4. 4.
    Yao, H., Wang, S., Zhang, X.: Detect piecewise linear contrast enhancement and estimate parameters using spectral analysis of image histogram. p. 94–97, (2009).Google Scholar
  5. 5.
    Chi-Chia, S., Shanq-Jang, R., Mon-Chau, S., Tun-Wen, P.: Dynamic contrast enhancement based on histogram specification. IEEE Trans.Consum. Electron 51(4), p. 1300–1305, (2005).Google Scholar
  6. 6.
    Munteanu, C., Rosa, A.: Towards automatic image enhancement using genetic algorithms. In: Evolutionary Computation, Proceedings of the 2000 Congress on, vol. 2, p. 1535–1542, IEEE, (2000).Google Scholar
  7. 7.
    Saitoh, F.: Image contrast enhancement using genetic algorithm. In: Systems, Man, and Cybernetics, 1999. IEEE SMC’99 Conference Proceedings. 1999 IEEE International Conference on, vol. 4, p. 899–904, IEEE, (1999).Google Scholar
  8. 8.
    Munteanu, C., Rosa, A.: Gray-scale image enhancement as an automatic process driven by evolution. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 34, no. 2, p. 1292–1298, (2004).Google Scholar
  9. 9.
    Kennedy, J., Eberhart R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Network, p. 1948–1995, (1995).Google Scholar
  10. 10.
    Ahmed, M.M., Zain J.M.: A study on the validation of histogram equalization as a contrast enhancement technique. In Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on, p. 253–256. IEEE, 2012.Google Scholar
  11. 11.
    Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 1, no. 4, p. 330–343, (2010).Google Scholar
  12. 12.
    Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. Evolutionary Computation, IEEE Transactions on 6, no. 1, p. 58–73, (2002).Google Scholar
  13. 13.
    Hassanzadeh, T., Vojodi, H., Mahmoudi, F.: Non-linear grayscale image enhancement based on firefly algorithm. In: Evolutionary and Memetic Computing, vol 7077, p. 174–181.Springer, Berlin Heidelberg, (2011).Google Scholar
  14. 14.
    Sarangi, P.P., Mishra, B., Dehuri, S.: Gray-level image enhancement using differential evolution optimization algorithm. In: Signal Processing and Integrated Networks (SPIN), (2014).Google Scholar
  15. 15.
    Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, p. 210–214, IEEE, (2009).Google Scholar
  16. 16.
    Ghosh, S., Roy, S., Kumar, U., Mallick, A.: Gray Level Image Enhancement Using Cuckoo Search Algorithm. In: Advances in Signal Processing and Intelligent Recognition Systems, p. 275–286, Springer International Publishing, (2014).Google Scholar
  17. 17.
    Bouaziz, A., Draa, A., Chikhi, S.: A Cuckoo search algorithm for fingerprint image contrast enhancement. In: Complex Systems (WCCS), 2014 Second World Conference on, p. 678–685, IEEE, (2014).Google Scholar
  18. 18.
    Zheng, H., Zhou, Y.: A Novel Cuckoo Search Optimization Algorithm Base on Gauss Distribution. Journal of Computational Information Systems 8:10, p. 4193–4200, (2012).Google Scholar
  19. 19.
  20. 20.
    Bunzuloiu, V., Ciuc, M., Rangayyan, R.M., Vertan, C.:Adaptive neighbourhood histogram equalization of color images. J. Electron Imaging 10(2), p. 445–449, (2001).Google Scholar
  21. 21.
    Yang X.S.: Nature-Inspired Metaheuristic Algorithms, Luniver Press, (2008).Google Scholar
  22. 22.
    Senthilnath, J.: Clustering Using Levy Flight Cuckoo Search. In: Proceedings of Seventh International Conference on Bio-Inspired Computing, vol. 202, p. 65–75, (2013).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Guru Nanak Dev UniversityAmritsarIndia

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