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Differential evolution optimization combined with chaotic sequences for image contrast enhancement

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  • Coelho, Leandro dos Santos
  • Sauer, João Guilherme
  • Rudek, Marcelo

Abstract

Evolutionary Algorithms (EAs) are stochastic and robust meta-heuristics of evolutionary computation field useful to solve optimization problems in image processing applications. Recently, as special mechanism to avoid being trapped in local minimum, the ergodicity property of chaotic sequences has been used in various designs of EAs. Three differential evolution approaches based on chaotic sequences using logistic equation for image enhancement process are proposed in this paper. Differential evolution is a simple yet powerful evolutionary optimization algorithm that has been successfully used in solving continuous problems. The proposed chaotic differential evolution schemes have fast convergence rate but also maintain the diversity of the population so as to escape from local optima. In this paper, the image contrast enhancement is approached as a constrained nonlinear optimization problem. The objective of the proposed chaotic differential evolution schemes is to maximize the fitness criterion in order to enhance the contrast and detail in the image by adapting the parameters using a contrast enhancement technique. The proposed chaotic differential evolution schemes are compared with classical differential evolution to two testing images. Simulation results on three images show that the application of chaotic sequences instead of random sequences is a possible strategy to improve the performance of classical differential evolution optimization algorithm.

Suggested Citation

  • Coelho, Leandro dos Santos & Sauer, João Guilherme & Rudek, Marcelo, 2009. "Differential evolution optimization combined with chaotic sequences for image contrast enhancement," Chaos, Solitons & Fractals, Elsevier, vol. 42(1), pages 522-529.
    • Handle: RePEc:eee:chsofr:v:42:y:2009:i:1:p:522-529
    DOI: 10.1016/j.chaos.2009.01.012
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    References listed on IDEAS

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    1. Salman, Ayed & Engelbrecht, Andries P. & Omran, Mahamed G.H., 2007. "Empirical analysis of self-adaptive differential evolution," European Journal of Operational Research, Elsevier, vol. 183(2), pages 785-804, December.
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    4. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Huang, De-Xian & Tang, Fang, 2007. "Control and synchronization of chaotic systems by differential evolution algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 412-419.
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    Cited by:

    1. Jun Guo & Jianzhong Zhou & Qiang Zou & Yi Liu & Lixiang Song, 2013. "A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2923-2946, June.
    2. Sushil Kumar & Millie Pant & Amiya Kumar Ray, 2018. "DE-IE: differential evolution for color image enhancement," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(3), pages 577-588, June.

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