摘要
针对已有图像融合方法会导致融合图像亮度不均匀,与原图像对比度不一致,不适合人眼视觉效果的缺点,提出将DCT稀疏表示与双通脉冲耦合神经网络相结合的压缩感知域图像融合算法。首先结合图像DCT稀疏表示的特点,设计射线采样矩阵;再对测量值采用基于测量值的信息熵加权平均融合;最后经过全变分优化算法对融合测量值重构得到融合图像。通过对多组不同类型传感器所获图像融合实验的主观视觉分析和客观评价表明,该算法所得的融合图像能从原始图像中获取更多有用信息,更好地保持原图像的边缘信息,从而获得更好的视觉效果。
The existing image fusion method results in uneven image brightness, not agreeing with the original image contrast, not suitable for the human eye visual defects. To solve this problem, a new algorithm based on compressive sensing, which combined the DCT sparse representation with the Dual-channel pulse coupled neural network mode, is offered in this paper. First, for the character of the DCT sparse representation, Radial Sampling Matrix is designed. Second, the measurements based on the weighted average is fused with the information entropy of measurements. Finally, the total variation algorithm is used tore construct the fusion image. Experiments have been done to fuse multiple sets of different types of sensor image. Both subjective visual analysis and objective evaluation criteria show that the proposed algorithm can obtain more useful information from the original image, keep the edge information of original image, and get a better visual effect.
出处
《红外技术》
CSCD
北大核心
2015年第4期283-288,共6页
Infrared Technology
基金
重庆市基础与前沿研究计划项目
编号:cstc2013jcyj A40045)
重庆市高校创新团队建设计划项目
编号:KJTD201343
关键词
压缩感知
双通道脉冲耦合神经网络
信息熵
全变分优化算法
compressive sensing
dual-channel pulse coupled neural network
information entropy
the total variation algorithm
