Pulse-coupled neural networks (PCNN) often cannot achieve optimum efficiency in image fusion because of parameter-setting problems of the PCNN model. The PCNN model has many parameters

all of which are intrinsically linked to overcome PCNN parameter-setting problems. A novel technique that uses PCNN model parameters of multi-objective particle swarm optimization (PSO)

is presented in this paper. The method consists of three steps. First

the PCNN model parameters are optimized using multi-objective PSO

and the optimal PCNN model is obtained. Then

the paper uses dual-tree complex wavelet transform (DTCWT) for multi-scale decomposition of source images. High-frequency image components are processed by the optimal PCNN model while low-frequency image components are fused by sum of modified Laplacian(SML). Finally

the fused image is reconstructed based on inverse DTCWT. Compared with many fusion methods

such as DTCWT

Laplace pyramid algorithm

and non-subsample contourlet transform

quantitative analysis is conducted for the fused image under indexes

such as mutual information

entropy

image quality factor

and standard deviation. The proposed method can obtain better visual effects

and higher values of edge information retention and mutual image information. Image fusion is an important research field in image processing technology. This study proposes a novel method that combines PSO and PCNN to complete image fusion. Experimental results show that the proposed method performs effectively in image fusion

which can be applied to the fields of computer vision

medicine

and remote sensing.