基于NSCT和改进型PCNN的红外与可见光图像融合算法

Infrared and Visible Image Fusion Method Based on NSCT and Improved PCNN

针对现有红外与可见光图像融合算法中易出现目标信息丢失或减弱的情况,提出了一种基于非下采样Contourlet变换和改进型脉冲耦合神经网络的融合算法。该算法首先对经过预处理和图像配准后的红外和可见光图像进行非下采样Contourlet变换,分别得到两幅图像的高频系数和低频系数;其次,采用改进型脉冲耦合神经网络对源图像高频系数进行融合,用区域能量最大处理低频系数;最后,对融合后的系数进行非下采样Contourlet反变换,得到融合后的图像。实验结果表明,本文算法在主观视觉上显示了更多的图像细节信息,同时客观数据指标也有不同程度的提升。

Since target information is easy to be lost or weaken in the current infrared and visible image fusion algorithms, a fusion algorithm based on non-subsampled contourlet transform and an improved pulse coupled neural network is proposed. First, the infrared and visible images which are preprocessed and registered are transformed through non-subsampled contourlet transform and the high-frequency coefficients and low-frequency coefficients of two images are obtained respectively. Then, the improved pulse coupled neural network is used to fuse the high-frequency coefficients of the images and the largest energy in a region is used to deal with the low-frequency coefficients. Finally, the fused coefficients are transformed by using NSCT inverse transform, so as to obtain the fused image. The experimental results show that the proposed algorithm can display more detail information of images in the subjective vision while its several objective data indicators are improved to a different extent.