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Multi-Objective Swarm Intelligent Systems pp 151–178Cite as

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

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Part of the book series: Studies in Computational Intelligence ((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.

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References

  1. Pohl, C., Genderen, J.L.V.: Multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sens. 19(5), 823–854 (1998)

    Article  Google Scholar 

  2. Piella, G.: A general framework for multiresolution image fusion: from pixels to regions. Inf. Fusion. 4(4), 259–280 (2003)

    Article  Google Scholar 

  3. Petrovic, V.S., Xydeas, C.S.: Gradient-based multiresolution image fusion. IEEE Trans. Image Process 13(2), 228–237 (2004)

    Article  Google Scholar 

  4. Choi, M., Kim, R.Y., Nam, M.R., et al.: Fusion of multispectral and panchromatic satellite images using the curvelet transform. IEEE Trans. Geosci. Remote Sens. Lett. 2(2), 136–140 (2005)

    Article  Google Scholar 

  5. De, I., Chanda, B.: A simple and efficient algorithm for multifocus image fusion using morphological wavelets. Signal Process 86(5), 924–936 (2006)

    Article  MATH  Google Scholar 

  6. Wang, Z.J., Ziou, D., Armenakis, C., et al.: A comparative analysis of image fusion methods. IEEE Trans. Geosci. Remote Sens. 43(6), 1391–1402 (2005)

    Article  Google Scholar 

  7. Nasrabadi, N.M., Clifford, S., Liu, Y.: Integration of stereo vision and optical flow using an energy minimization approach. J. Opt. Sot. Amer. A 6(6), 900–907 (1989)

    Article  Google Scholar 

  8. Qin, Z., Bao, F.M., Li, A.G., et al.: 2004 Digital image fusion. Xi’an Jiaotong University Press, Xi’an (in Chinese)

    Google Scholar 

  9. Niu, Y.F., Shen, L.C.: A novel approach to image fusion based on multi-objective optimization. In: Proceedings of IEEE WCICA 2006, Dalian, pp. 9911–9915 (2006)

    Google Scholar 

  10. Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)

    Article  Google Scholar 

  11. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. Technical Report 103, Gloriastrasse 35, CH-8092 Zurich, Switzerland (2001)

    Google Scholar 

  12. Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  13. Li, X.: A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 37–48. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  14. Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)

    Article  Google Scholar 

  15. Sierra, M.R., Coello, C.C.A.: Improving PSO-based multi-objective optimization using crowding, mutation and e-dominance. In: Coello, C.C.A., et al. (eds.) EMO 2005. LNCS, vol. 3410, pp. 505–519. Springer, Heidelberg (2005)

    Google Scholar 

  16. Hassan, R., Cohanim, B., Weck, O.: A comparison of particle swarm optimization and the genetic algorithm. In: Proceedings of 46th AIAA Structures, Structural Dynamics and Materials Conference, Austin, Texas (2005)

    Google Scholar 

  17. Niu, Y.F., Shen, L.C.: Multiobjective Constriction Particle Swarm Optimization and Its Performance Evaluation. In: Huang, D.S. (ed.) ICIC 2007. LNCS (LNAI), vol. 4682, pp. 1131–1140. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Graps, A.: An Introduction to Wavelets. IEEE Computational Science and Engineering 02(2), 50–61 (1995)

    Article  Google Scholar 

  19. Mallat, S.: A Theory of Multiresolution Signal Decomposition: the Wavelet Representation. IEEE Trans. PAMI 11(7), 674–693 (1989)

    MATH  Google Scholar 

  20. Huang, X.S., Chen, Z.: A wavelet-based scene image fusion algorithm. In: Proceedings of IEEE TENCON 2002, Beijing, pp. 602–605 (2002)

    Google Scholar 

  21. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  22. Wang, Z., Bovik, A.C., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process 13(4), 600–612 (2004)

    Article  Google Scholar 

  23. Qu, G.H., Zhang, D.L., Yan, P.F.: Information measure for performance of image fusion. Electron Lett. 38(7), 313–315 (2002)

    Article  Google Scholar 

  24. Ramesh, C., Ranjith, T.: Fusion performance measures and a lifting wavelet transform based algorithm for image fusion. In: Proceedings of FUSION 2002, Annapolis, pp. 317–320 (2002)

    Google Scholar 

  25. Wang, Q., Shen, Y., Zhang, Y., et al.: Fast quantitative correlation analysis and information deviation analysis for evaluating the performances of image fusion techniques. IEEE Trans. Instmm. Meas. 53(5), 1441–1447 (2004)

    Article  Google Scholar 

  26. Tsagaris, V., Anastassopoulos, V.: A global measure for assessing image fusion methods. Opt. Eng. 45(2), 1–8 (2006)

    Article  Google Scholar 

  27. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE ICNN, Perth, pp. 1942–1948 (1995)

    Google Scholar 

  28. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Mateo (2001)

    Google Scholar 

  29. Ray, T., Liew, K.M.: A swarm metaphor for multiobjective design optimization. Eng. Optim. 34(2), 141–153 (2002)

    Article  Google Scholar 

  30. Ho, S.L., Yang, S.Y., et al.: A particle swarm optimization-based method for multiobjective design optimizations. IEEE Trans. Magn. 41(5), 1756–1759 (2005)

    Article  Google Scholar 

  31. Kennedy, J., Mendes, R.: Neighborhood Topologies in Fully Informed and Best-of-Neighborhood Particle Swarms. IEEE Trans. SMC Pt C: Appl. Rev. 4, 515–519 (2006)

    Article  Google Scholar 

  32. Fang, K.T., Ma, C.X.: Orthogonal and uniform experimental design. Science Press, Beijing (2001)

    Google Scholar 

  33. Leung, Y.W., Wang, Y.P.: Multiobjective programming using uniform design and genetic algorithm. IEEE Trans. SMC Pt C Appl. Rev. 30(3), 293–304 (2000)

    Google Scholar 

  34. Reyes-Sierra, M., Coello, C.C.A.: Multi-objective Particle Swarm Optimizers: a Survey of the State-of-the-Art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006)

    MathSciNet  Google Scholar 

  35. Bergh, F.V.D., Engelbrecht, A.P.: A Study of Particle Swarm Optimization Particle Trajectories. Information Sciences 176(8), 937–971 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  36. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithm Research: A History and Analysis. Air Force Inst Technol, Wright-Patterson AFB, OH, Tech Rep. TR-98-03 (1998)

    Google Scholar 

  37. Czyzak, P., Jaszkiewicz, A.: Pareto-Simulated Annealing: A Metaheuristic Technique for Multi-Objective Combinatorial Optimization. Journal of Multi-Criteria Decision Analysis 7(1), 34–47 (1998)

    Article  MATH  Google Scholar 

  38. Schott, J.R.: Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. M S Thesis, Massachusetts Inst. Technol., MA (1995)

    Google Scholar 

  39. Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classifications, Analyzes, and New Innovations. Ph D Dissertation, Graduate School of Eng., Air Force Inst. Technol., Wright-Patterson AFB, OH (1999)

    Google Scholar 

  40. Kita, H., Yabumoto, Y., et al.: Multi-objective Optimization by Means of The Thermodynamical Genetic Algorithm. In: Voigt, H.M., et al. (eds.) PPSN 1996. LNCS, vol. 1141. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  41. Eberhart, R.C., Shi, Y.: Particle swarm optimization: development, applications and resources. In: Proceedings of IEEE CEC, Seoul, pp. 81–86 (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, 410073, China

    Yifeng Niu & Lincheng Shen

  2. Equipment Academy of Air Force, Beijing, 100085, China

    Xiaohua Huo

  3. College of Mathematics and Computer Science, Hunan Normal University, Changsha, 410081, China

    Guangxia Liang

Authors
  1. Yifeng Niu
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  2. Lincheng Shen
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  3. Xiaohua Huo
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  4. Guangxia Liang
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Editors and Affiliations

  1. Faculdade de Engenharia, Universidade do Estado do Rio de Janeiro, sala 5022-D Rua São Francisco Xavier 524, 20550-900, MARACANÃ, RJ, Brazil

    Nadia Nedjah  & Luiza de Macedo Mourelle  & 

  2. Universidade Federal do Paraná Departamento de Engenharia Elétrica Pós-Graduação em Engenharia Elétrica Centro Politécnico 81531-980, Curitiba-PR, Brazil and Pontifícia Universidade Católica do Paraná Centro, Brazil

    Leandro dos Santos Coelho

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Niu, Y., Shen, L., Huo, X., Liang, G. (2010). Multi-Objective Wavelet-Based Pixel-Level Image Fusion Using Multi-Objective Constriction Particle Swarm Optimization. In: Nedjah, N., dos Santos Coelho, L., de Macedo Mourelle, L. (eds) Multi-Objective Swarm Intelligent Systems. Studies in Computational Intelligence, vol 261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05165-4_7

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  • DOI: https://doi.org/10.1007/978-3-642-05165-4_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05164-7

  • Online ISBN: 978-3-642-05165-4

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