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An Adaptive Multi-objective Particle Swarm Optimization for Color Image Fusion

  • Yifeng Niu
  • Lincheng Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

A novel algorithm of adaptive multi-objective particle swarm optimization (AMOPSO-II) is proposed and used to search the optimal color image fusion parameters, which can achieve the optimal fusion indices. First the algorithm of AMOPSO-II is designed; then the model of color image fusion in YUV color space is established, and the proper evaluation indices are given; and finally AMOPSO-II is used to search the optimal fusion parameters. AMOPSO-II uses a new crowding operator to improve the distribution of nondominated solutions along the Pareto front, and uses the uniform design to obtain the optimal combination of the parameters of AMOPSO-II. Experimental results indicate that AMOPSO-II has better exploratory capabilities than MOPSO and AMOPSO-I, and that the approach to color image fusion based on AMOPSO-II realizes the Pareto optimal color image fusion.

Keywords

Particle Swarm Optimization Discrete Wavelet Transform Pareto Front Multiobjective Optimization Image Fusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yifeng Niu
    • 1
  • Lincheng Shen
    • 1
  1. 1.College of Mechatronic Engineering and AutomationNational University of Defense TechnologyChangshaChina

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