基于交替梯度滤波器和改进PCNN的红外与可见光图像融合

Infrared and visible image fusion based on alternating gradient filter and improved PCNN

为了克服红外与可见光图像融合过程中在目标物体的边缘处产生虚影的问题,提出一种基于交替梯度滤波器和改进脉冲耦合神经网络的图像融合方法。在梯度滤波器的基础上结合滚动引导滤波器和平滑迭代恢复滤波器提出一种交替梯度滤波器,可以同时实现小结构消除,局部强度保持和边缘恢复的特性。利用交替梯度滤波器分解源图像,分解为近似层和残差层。近似层采用多尺度形态学算子和最大区域能量与源图像相结合的融合规则,残差层用改进参数自适应脉冲耦合神经网络融合规则进行融合。最后,经过交替梯度滤波器重构得到融合结果图。实验结果表明,与其他5种融合方法进行比较,本文方法的客观评价指标平均梯度、标准差、信息熵、空间频率、边缘强度和视觉保真度分别平均提高了18%,10%,2.8%,16%,51%,11.2%,且能够避免在目标物体的边缘处产生虚影,较好地保留源图像的亮度、边缘、细节及纹理等信息。

To overcome the problem of image blur at the edge of object in the process of infrared and visible image fusion,an image fusion method based on the alternating gradient filter and improved pulse coupled neural network(PCNN)was proposed.In this paper,a novel alternating gradient filter(AGF)was proposed based on the gradient filter,which combines the rolling guide filter(RGF)and the smooth iterative recovery filter(SIRmed),with corresponding characteristics of local strength retention and edge recovery.The source images were decomposed into the approximate layer and residual layer using the AGF.The multi-scale morphological operator and the fusion rule of maximum region energy for the source images were adopted for the approximation layer,and then the residual layer was fused with the im‐proved parameter adaptive PCNN fusion rule.Finally,the fusion result was reconstructed by an alternating gradient filter.The experimental results show that compared with the other five fusion methods,the objective evaluation indices of this method,that is the average gradient(AG),standard deviation(STD),information entropy(EN),spatial frequency(SF),edge intensity(EI),and visual fidelity(VIFF)increase by 18%,10%,2.8%,16%,51%,and 11.2%,respectively.In addition,the results demonstrate that the proposed AGF fusion method can overcome the shadow on the edge of the object and reserve the brightness,edge,detail,and texture information of the source images.