结合视觉显著性与Dual PCNN的红外与可见光图像融合

针对现存的红外与可见光图像融合算法亮度不均、目标不突出、对比度不高、细节丢失等问题,结合非下采样剪切波变换(NSST)具有多尺度、最具稀疏表达的特性,显著性检测具有突出红外目标的优势,双通道脉冲耦合神经网络(Dual-PCNN)具有耦合、脉冲同步激发等优点,提出一种基于NSST结合视觉显著性引导Dual-PCNN的图像融合方法。首先,通过NSST分解红外与可见光图像各方向的高频与低频子带系数；然后,低频子带系数采用基于显著性决策图引导Dual-PCNN融合策略,高频子带系数采用改进的空间频率作为优化Dual-PCNN的激励进行融合；最后,经过NSST逆变换得到融合图像。实验结果表明,融合图像红外目标突出且可见光背景细节丰富。该方法相比于其他融合算法在主观评价与客观评价上都有一定程度的改善。

Infrared and Visible Images Fusion Using Visual Saliency and Dual-PCNN

Aiming at uneven brightness,inconspicuous object,low contrast and loss details problems in the existing infrared and visible light image fusion methods,in combination with nonsubsampled shearlet transform (NSST) which has multi-scale transformation and the most sparse expression characteristics,saliency detection which has the advantage of highlighting infrared objects,and Dual-channel pulse coupled neural network(Dual-PCNN)which has the advantages of coupling and pulse synchronization,an image fusion method for infrared and visible light images based on NSST and visual saliency guide Dual-PCNN was proposed in this paper.Firstly,the high frequency and low frequency sub-band coefficients of infrared and visible light image are decomposed by NSST in each direction,and then low frequency coefficients are fused by the Dual-PCNN,which is guided by the saliency map of the images.For the high frequency sub-band coefficients,a modified spatial frequency is adopted as the input to motivate the Dual-PCNN.Finally,the fused image is reconstructed by inverse NSST.The experimental results demonstrate that the infrared objects in the fusion image are highlighted and the details of the visible background are rich.Compared with other fusion algorithms,the proposed method has a certain degree of improvement on the subjective evaluation and objective evaluation.