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Multi-Objective Wavelet-Based Pixel-Level Image Fusion Using Multi-Objective Constriction Particle Swarm Optimization

  • Yifeng Niu
  • Lincheng Shen
  • Xiaohua Huo
  • Guangxia Liang
Part of the Studies in Computational Intelligence book series (SCI, volume 261)

Abstract

In most methods of pixel-level image fusion, determining how to build the fusion model is usually based on people’s experience, and the configuration of fusion parameters is somewhat arbitrary. In this chapter, a novel method of multi-objective pixel-level image fusion is presented, which can overcome the limitations of conventional methods, simplify the fusion model, and achieve the optimal fusion metrics. First the uniform model of pixel-level image fusion based on discrete wavelet transform is established, two fusion rules are designed; then the proper evaluation metrics of pixel-level image fusion are given, new conditional mutual information is proposed, which can avoid the information overloaded; finally the fusion parameters are selected as the decision variables and the multi-objective constriction particle swarm optimization (MOCPSO) is proposed and used to search the optimal fusion parameters. MOCPSO not only uses mutation operator to avoid earlier convergence, but also uses a new crowding operator to improve the distribution of nondominated solutions along the Pareto front, and introduces the uniform design to obtain the optimal parameter combination. The experiments of MOCPSO test, multi-focus image fusion, blind image fusion, multi-resolution image fusion, and color image fusion are conducted. Experimental results indicate that MOCPSO has a higher convergence speed and better exploratory capabilities than MOPSO, especially when the number of objectives is large, and that the fusion method based on MOCPSO is is suitable for many types of pixel-level image fusion and can realize the Pareto optimal image fusion.

Keywords

Particle Swarm Optimization Discrete Wavelet Transform Pareto Front Image Fusion Source Image 
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 2010

Authors and Affiliations

  • Yifeng Niu
    • 1
  • Lincheng Shen
    • 1
  • Xiaohua Huo
    • 2
  • Guangxia Liang
    • 3
  1. 1.College of Mechatronic Engineering and AutomationNational University of Defense TechnologyChangshaChina
  2. 2.Equipment Academy of Air ForceBeijingChina
  3. 3.College of Mathematics and Computer ScienceHunan Normal UniversityChangshaChina

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