Introduction - If you have any usage issues, please Google them yourself
In this paper, a multimodal image fusion algorithm based on multiresolution
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).