基于改进PCNN的压缩感知域图像融合

Image fusion algorithm based on improved PCNN in compressed sensing domain

提出了一种压缩感知域基于改进PCNN的图像融合算法。根据压缩采样得到待融合图像的压缩观测值,建立改进PCNN模型,即利用压缩观测值的物理意义对PCNN中连接系数,加权矩阵和特征参数进行自适应设定。并将压缩观测值作为改进PCNN神经元输入获取点火映射图作为融合算子,对源图像观测值融合后进行重构得到融合图像。实验结果表明,该算法克服现有压缩感知图像融合算法细节丢失的缺陷,主客观评价优于现有压缩感知图像融合算法。

An image fusion algorithm based on improved PCNN is proposed in compressive sensing domain. Original images are compressed by compressed sensing to obtain measurements. Improved PCNN model is built based on physical significance of measurements. Namely link coefficient,weighting matrix and threshold amplification coefficient are set adaptively. Measurements are selected as input neuron to obtain ignition-map as fusion operator. Fusion image is obtained according to reconstructing algorithm. The results show that this algorithm overcomes the detail missing of traditional compressed sensing image fusion,and the subjective and objective evaluation indexes show that this method is superior to the existing compressed sensing image fusion methods.