摘要
针对同一场景的红外与可见光图像融合,提出了一种基于非采样Contourlet变换(NSCT)和改进的脉冲耦合神经网络(IPCNN)的图像融合新算法。首先利用NSCT对图像进行多尺度、多方向稀疏分解,然后针对各带通方向高频子带系数的选择,提出了一种应用IPC-NN计算图像匹配度的融合策略。实验结果表明,该算法能够很好地将红外图像与可见光图像中的重要信息提取并注入到融合图像中,与其他方法相比较,取得了更好的融合效果,提高了融合图像的质量。
Focusing on infrared and visible image fusion of the same scene, a new image fusion algorithm based on nonsubsampled contourlet transform （NSCT）and improved pulse-coupled neural networks （IPCNN）was proposed. Firstly image was decomposed sparse with various scales and directional features using NSCT, and then a calculation matching selection principle with the varieties of directional bandpass subband coefficients based on IPCNN was devel- oped. Compared with other methods, the results of experiment show that the proposed algorithm can infuse important information of infrared and visible image to fusion image, acquire better results, and improve quality of fusion image.
出处
《激光与红外》
CAS
CSCD
北大核心
2009年第1期92-96,共5页
Laser & Infrared
基金
安徽省重点实验室基金(No.2007A0103013Y)资助
