Gray Level Image Enhancement by Improved Differential Evolution Algorithm

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 202)

Abstract

In this paper, an enhanced version of DE named MRLDE is used to solve the problem of image enhancement. The parameterized transformation function is used for image enhancement which uses both local and global information of image. For image enhancement, an objective criterion is considered which use the entropy and edge information of image. The objective of the DE is to maximize the objective fitness criterion in order to improve the contrast. Results of MRLDE are compared with basic DE, PSO, GA and with histogram equalization (HE) which is another popular enhancement technique. The obtained results indicate that proposed MRLDE yield better performance in the comparison of other techniques.

Keywords

Image enhancement Differential evolution Mutation Parameter optimization 

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References

  1. 1.
    Gonzales R C, Woods, R. E.: Digital Image Processing. New York: Addison-Wesley (1987).Google Scholar
  2. 2.
    Gonzalez, R.C., Fittes, B.A.: Gray-level transformations for interactive image enhancement. Mechanism and Machine Theory, 12, 111-122 (1977).Google Scholar
  3. 3.
    Gorai, A., Ghosh, A.: Gray level image enhancement by particle swarm optimization. Proceeding of IEEE (2009).Google Scholar
  4. 4.
    Poli, R., Cagnoni, S.: Evolution of pseudo-coloring algorithms for image enhancement. Univ. Birmingham, Birmingham, U.K., Tech. Rep. CSRP-97-5 (1997).Google Scholar
  5. 5.
    Munteanu, C., Lazarescu, V.: Evolutionary contrast stretching and detail enhancement of satellite images. In Proc. Mendel, Berno, Czech Rep., pp. 94-99 (1999).Google Scholar
  6. 6.
    Munteanu, C., Rosa, A.: Evolutionary image enhancement with user behavior modeling. ACM SIGAPP Applied Computing Review,9(1), 8-14 (2001).Google Scholar
  7. 7.
    Saitoh, F.: Image contrast enhancement using genetic algorithm. In Proc. IEEE SMC, Tokyo, Japan, pp. 899-904 (1993).Google Scholar
  8. 8.
    Pal, S.K., Bhandari, D., Kundu, M.K.: Genetic algorithms for optimal image enhancement. Pattern Recognition Letter, 15, 261-271 (1994).Google Scholar
  9. 9.
    Braik, M., Sheta, A., Ayesh, A.: Image enhancement using particle swarm optimization. In Proc of the World Congress on Engineering (WCE-2007), London UK (2007).Google Scholar
  10. 10.
    Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization and evolutionary algorithms on numerical benchmark problems. Congress on Evolutionary Computation, pp. 980-987 (2004).Google Scholar
  11. 11.
    Plagianakos, V., Tasoulis, D., Vrahatis M.,: A review of major application areas of differential evolution. In: Advances in differential evolution, Springer, Berlin, vol. 143, pp 197–238 (2008).Google Scholar
  12. 12.
    Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev. 33 (1–2), 61–106 (2010).Google Scholar
  13. 13.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Transaction of Evolutionary Computing. 15(1), 4-13 (2011).Google Scholar
  14. 14.
    Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous. Spaces. Berkeley, CA, Tech. Rep. TR-95-012 (1995).Google Scholar
  15. 15.
    Kumar, P., Pant, M.: Enhanced mutation strategy for differential evolution. In: Proc of IEEE Congress on Evolutionary Computation (CEC 12) (2012).Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Applied Sciences and EngineeringIIT RoorkeeRoorkeeIndia

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