结合NSDTCT和压缩感知PCNN的图像融合算法

Image Fusion Algorithm Based on Nonsubsampled Dual-tree Complex Contourlet Transform and Compressive Sensing Pulse Coupled Neural Network

针对基于传统小波变换的图像融合算法存在的不足,提出一种基于非下采样双树复轮廓波变换(NSDTCT)和压缩感知脉冲耦合神经网络(CS-PCNN)的图像融合算法.首先将源图像经过NSDTCT分解后得到低频子带及高频子带系数;对于低频子带系数,提出了基于区域平均梯度、区域能量和S函数相结合的自适应加权融合规则;对于数据量较大的高频子带系数,提出了基于CS-PCNN理论的融合规则,并将改进的拉普拉斯能量和作为PCNN的外部输入;最后对融合系数进行NSDTCT逆变换,得到融合图像.实验结果表明,该算法可以有效地提高图像融合的计算效率和质量,在视觉效果及客观指标上均优于一些经典的融合算法.

To overcome the shortages of image fusion method based on traditional wavelet transform, a novel image fusion algorithm based on non-subsampled dual-tree complex contourlet transform(NSDTCT) and com-pressive sensing pulse coupled neural network(CS-PCNN) is proposed. Firstly, decompose the source images by NSDTCT to obtain the low frequency sub-band coefficients and high frequency sub-band coefficients. For the low frequency sub-band coefficients, an adaptive weighted fusion method combining the regional average gradi-ent, regional energy with Sigmoid function is presented. For the high frequency sub-band coefficients with large amount of data, a fusion rule based on the theory of CS-PCNN is presented, and the novel sum-modified Lapla-cian is used for the external input of PCNN. Finally, the fused image is obtained by performing the inverse NSDTCT on the fused coefficients. The experimental results show that the proposed algorithm can improve the computation efficiency and the quality of the fused image, and outperforms other classical fusion algorithms in terms of both visual quality and objective evaluation.