多目标粒子群优化PCNN参数的图像融合算法

目的 脉冲耦合神经网络（PCNN）在图像融合上往往因为参数设置问题而达不到最佳效果，为了提高图像融合的质量，提出了一种基于多目标粒子群优化PCNN参数的图像融合算法。方法 首先用多目标粒子群对PCNN模型参数进行优化得到最优PCNN参数模型，然后利用双复树小波（DTCWT）对图像多尺度分解，将高频分量通过优化好的PCNN模型进行高频融合，低频分量通过拉普拉斯分量绝对和（SML）来进行低频融合，最后通过DTCWT逆变换实现图像的融合。结果 分别与DTCWT，拉普拉斯金字塔变换（LP）以及非下采样Contourlet变换（NSCT）进行实验对比，融合图像Clock，Lab的融合结果在客观指标上的互信息（8.062 3，7.908 5）、图像的品质因数（0.716 2,0.714 2）和标准差（51.213,47.671）都优于其他方法，熵和其他方法差不多，融合结果能够获得更好的视觉效果以及较大的互信息值和边缘信息保留值。结论 该方法有较好融合图像的能力，可适用于计算机视觉、医学、遥感等领域。

Image fusion algorithm using PCNN model parameters of multi-objective particle swarm optimization

Objective 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. Method 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. Result 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. Conclusion 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.