Abstract
In this paper, a multimodal image fusion algorithm based on multi-resolution transform and particle swarm optimization (PSO) is proposed. Firstly, the source images are decomposed into low-frequency coefficients and high-frequency coefficients by the dual-tree complex wavelet transform (DTCWT). Then, the high-frequency coefficients are fused by the maximum selection fusion rule. The low-frequency coefficients are fused by weighted average method based on regions, and the weights are estimated by the PSO to gain optimal fused images. Finally, the fused image is reconstructed by the inverse DTCWT. The experiments demonstrate that the proposed image fusion method can illustrate better performance than the methods based on the DTCWT, the support value transform (SVT), and the nonsubsampled contourlet transform (NSCT).
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Tao, J., Li, S., Yang, B. (2010). Multimodal Image Fusion Algorithm Using Dual-Tree Complex Wavelet Transform and Particle Swarm Optimization. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_40
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DOI: https://doi.org/10.1007/978-3-642-14831-6_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
Online ISBN: 978-3-642-14831-6
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