Gray Level Image Enhancement Using Cuckoo Search Algorithm

  • Soham Ghosh
  • Sourya Roy
  • Utkarsh Kumar
  • Arijit Mallick
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 264)


In this work we have assessed the capability of a new optimization algorithm – the Cuckoo Search algorithm in tuning the image enhancement functions for peak performance. The assessment has been conducted in comparison to two of the old optimization algorithm aided enhancement, namely, Genetic Algorithms and Particle Swarm Optimization and previous enhancement techniques Histogram Equalization and Linear Contrast Stretch techniques. Results have been assimilated in this paper and conclusions have been drawn keeping the fitness of image and number of edgels in enhanced image as the benchmark. The results have illustrated the capability of Cuckoo search algorithm in optimizing the enhancement functions.


Cuckoo Search Optimization Edgels Enhancement Image fitness Levy flight Metaheuristic Algorithm 


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  1. 1.
    Gorai, A., Ghosh, A.: Gray-level Image Enhancement By Particle Swarm Optimization. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 72–77 (2009) Print ISBN: 978-1-4244-5053-4Google Scholar
  2. 2.
    Munteanu, C., Rosa, A.: Towards automatic image enhancement using Genetic Algorithms. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 1535–1542. Inst. Superior Tecnico, Univ. Tecnica de Lisboa, Portugal (2000)Google Scholar
  3. 3.
    Braik, M., Sheta, A.F., Ayesh, A.: Image Enhancement Using Particle Swarm Optimization. In: Proceedings of the World Congress on Engineering, WCE 2007, London, U.K, July 2-4, vol. I (2007) ISBN:978-988-98671-5-7Google Scholar
  4. 4.
    Singh, N., Kaur, M., Singh, K.V.P.: Parameter Optimization In Image Enhancement Using PSO. American Journal of Engineering Research (AJER) 2(5), 84–90, e-ISSN : 2320-0847 p-ISSN : 2320-0936Google Scholar
  5. 5.
    Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: Proc. of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), India, pp. 210–214. IEEE Publications, USA (2009)Google Scholar
  6. 6.
    Yang, X.-S., Deb, S.: Engineering Optimisation by Cuckoo Search. Int. J. Mathematical Modelling and Numerical Optimisation 1(4), 330–343 (2010)Google Scholar
  7. 7.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall PublicationsGoogle Scholar
  8. 8.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing using MATLAB, 2nd edn. Prentice HallGoogle Scholar
  9. 9.
    Mantegna, R.N.: Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. Phys. Rev. E 49(5), 4677–4683 (1994), doi:10.1103/PhysRevE.49.4677 Key: citeulike: 6592204Google Scholar
  10. 10.
    He, Y., Tian, J., Luo, X., Zhang, T.: Image enhancement and minutiae matching in fingerprint verification. Elsevier, Pattern Recognition Letters 24(9-10), 1349–1360 (2003)Google Scholar
  11. 11.
    Sezan, M.I., Tekalp, A.M., Schaetzing, R.: Automatic anatomically selective image enhancement in digital chest radiography. IEEE Trans. Med. Imag. 8, 154–162 (1989)Google Scholar
  12. 12.
    Pratt, W.K.: Digital Image Processing, 2nd edn. John Wiley and Sons (1991)Google Scholar
  13. 13.
    Castleman, K.R.: Digital Image Processing. Prentice Hall (1996)Google Scholar
  14. 14.
    Chaudhary, A., Vatwani, K., Agrawal, T., Raheja, J.L.: A Vision-Based Method to Find Fingertips in a Closed Hand. Journal of Information Processing Systems 8(3), 399–408 (2012)Google Scholar
  15. 15.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8) (August 1998)Google Scholar
  16. 16.
    Senthilnath, J.: Clustering Using Levy Flight Cuckoo Search. In: Proceedings of Seventh International Conference on Bio-Inspired Computing, vol. 202, pp. 65–75 (2013)Google Scholar
  17. 17.
    Saida, I.B., Nadjet, K., Omar, B.: A new algorithm for data clustering based on cuckoo search optimization. In: Pan, J.-S., Krömer, P., Snášel, V. (eds.) Genetic and Evolutionary Computing. AISC, vol. 238, pp. 55–64. Springer, Heidelberg (2014)Google Scholar
  18. 18.
    Rodrigues, D., Pereira, L.A.M., Almeida, T.N.S., Papa, J.P., Souza, A.N., Ramos, C.C.O., Yang, X.-S.: BCS: A Binary Cuckoo Search algorithm for feature selection. In: 2013 IEEE International Symposium on Circuits and Systems (ISCAS), May 19-23, pp. 465–468 (May 2013), doi:10.1109/ISCAS.2013.6571881Google Scholar
  19. 19.
    Pani, P.R., Nagpal, R.K., Malik, R., Gupta, N.: Design of planar EBG structures using cuckoo search algorithm for power/ground noise suppression. Progress In Electromagnetics Research M 28, 145–155 (2013), doi:10.2528/PIERM12121108Google Scholar
  20. 20.
    Aly, W.M., Sheta, A.: Parameter Estimation of Nonlinear Systems Using Lèvy Flight Cuckoo Search. Research and Development in Intelligent Systems XXX, 443–449 (2013), doi:10.1007/978-3-319-02621-3_33Google Scholar
  21. 21.
    Goel, S., Sharma, A., Bedi, P.: Journal Title - International Journal of Hybrid Intelligent Systems. Novel approaches for classification based on Cuckoo Search Strategy 10(3), 107–116 (2013), doi:10.3233/HIS-130169 (Issue Cover Date January 1, 2013)Google Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Soham Ghosh
    • 1
  • Sourya Roy
    • 1
  • Utkarsh Kumar
    • 1
  • Arijit Mallick
    • 1
  1. 1.Department of Instrumentation and Electronics EngineeringJadavpur UniversityKolkataIndia

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