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).