Abstract
Based on multi-objective optimization, a novel approach to blind image fusion (without the reference image) is presented in this paper, which can achieve the optimal fusion indices through optimizing the fusion parameters. First the proper evaluation indices of blind image fusion are given; then the fusion model in DWT domain is established; and finally the adaptive multi-objective particle swarm optimization (AMOPSO-II) is proposed and used to search the fusion parameters. AMOPSO-II not only uses an adaptive mutation and an adaptive inertia weight to raise the search capacity, but also uses a new crowding operator to improve the distribution of nondominated solutions along the Pareto front. Results show that AMOPSO-II has better exploratory capabilities than AMOPSO-I and MOPSO, and that the approach to blind image fusion based on AMOPSO-II realizes the optimal image fusion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Pohl, C., Genderen, J.L.V.: Multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sens. 5, 823–854 (1998)
Niu, Y.F., Shen, L.C.: A novel approach to image fusion based on multi-objective optimization. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian (in press, 2006)
Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evol. Comput. 2, 149–172 (2000)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm, TIK-Report 103, ETH, Zurich, Switzerland (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2, 182–197 (2002)
Li, X.: A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Cantu-Paz, E., et al. (eds.) Genetic and Evolutionary Computation, pp. 37–48. Springer, Berlin (2003)
Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 3, 256–279 (2004)
Sierra, M.R., Coello, C.A.: Improving PSO-based multi-objective optimization using crowding, mutation and e-dominance. In: Coello, C.A., et al. (eds.) Evolutionary Multi-Criterion Optimization, pp. 505–519. Springer, Berlin (2005)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 4, 600–612 (2004)
Wang, Z.J., Ziou, D., Armenakis, C., Li, D.R., Li, Q.Q.: A comparative analysis of image fusion methods. IEEE Trans. Geosci. Remote Sens. 6, 1391–1402 (2005)
Qu, G.H., Zhang, D.L., Yan, P.F.: Information measure for performance of image fusion. Electron. Lett. 7, 313–315 (2002)
Ramesh, C., Ranjith, T.: Fusion performance measures and a lifting wavelet transform based algorithm for image fusion. In: Proceedings of the 5th International Conference on Information Fusion, Annapolis, pp. 317–320 (2002)
Huang, X.S., Chen, Z.: A wavelet-based image fusion algorithm. In: Proceedings of the IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, Beijing, pp. 602–605 (2002)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Mateo (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Niu, Y., Shen, L., Bu, Y. (2006). Multi-objective Blind Image Fusion. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_104
Download citation
DOI: https://doi.org/10.1007/11795131_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
eBook Packages: Computer ScienceComputer Science (R0)