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.
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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
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DOI: https://doi.org/10.1007/11795131_104
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