摘要
为提高多聚焦图像的清晰度及PCNN在图像融合方面的实用性,提出一种基于NSCT与双通道PCNN的多聚焦图像融合新方法.首先对来自同一场景的多聚焦图像分别进行NSCT变换;然后将基于SML的视觉特性对比度用于低频子带系数的融合;对于高频子带则通过改进的空间频率激励双通道PCNN进行融合,两个通道对应不同的连接强度,根据点火次数确定高频子带融合系数;最后进行逆NSCT变换得到最终融合图像.实验结果表明,该融合算法得到的融合图像具有较高的清晰度,在主观和客观评价指标上优于其他算法,具有较好的视觉效果.
In order to improve the multi-focus image sharpness and the practicability of PCNN in the aspect of image fusion, a new multi-focus image fusion method was proposed based on nonsubsampled Contourlet transform （NSCT） and dual-channel pulse coupled neural network （DCPCNN）. Firstly, multi-focus images which from the same scene were decomposed using NSCT respectively; and then visual properties contrast based on SML was used to fuse low frequency suhband coefficients; High frequency sub-band component fused through DCPCNN which motivated by modified spatial frequency. DCPCNN had two channels corresponding to different linking strength, high frequency sub-band fusion coefficient was determined according to the number of ignition. Finally, the fusion image achieved by inverse transformation of NSCT. The experimental results demonstrate that the proposed algorithm improves fusion image sharpness significantly, subjective and objective indicators are better than other fusion algorithms, having better visual effects.
出处
《微电子学与计算机》
CSCD
北大核心
2016年第8期29-33,共5页
Microelectronics & Computer
基金
国家自然科学基金项目(61405144)
关键词
非下采样CONTOURLET变换
双通道PCNN
改进的空间频率
链接强度
多聚焦图像融合
nonsubsampled Contourlet transform （NSCT）
dual-channel pulse coupled neural network （DCPCNN）
modified spatial frequency
linking strength
multi-focus image fusion
