基于NSST域的自适应区域和SCM相结合的多聚焦图像融合

为了提高多聚焦图像的融合效果,结合多源图像之间的共享相似性,提出了一种基于非下采样Shearlet变换(Nonsubsampled Shearlet Transform,NSST)域的自适应区域与脉冲发放皮层模型(Spiking Cortical Model,SCM)结合的新型图像融合算法。首先用NSST分解源图像,然后计算边缘能量(Energy Of Edge,EOE),在自适应区域用投票加权法融合低频系数,高频系数由边缘能量作为输入的SCM点火图融合,最后通过逆NSST获得该融合图像。该算法既可以很好地保持源图像的信息,又可以抑制在变换域因非线性运算产生的像素失真。实验结果表明,该方法优于最新的变换域和脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)融合方法。

Multi-focus Image Fusion Using Adaptive Region and SCM Based on NSST Domain

In order to improve the fusion effect of multi-focus image,combined with the shared similarity among multiple source images,a new image fusion algorithm based on adaptive regions and spiking cortical model (SCM) of nonsubsampled shearlet transform (NSST) domain was proposed.First,NSST is utilized for decomposition of the source images.Then by calculating the energy of edge(EOE),the low frequency coefficients are fused by weight votes in adaptive regions.And the high frequency coefficients is fused by fired map of SCM which is motivated by EOE.Finally the fusion image is gained by inverse NSST.The algorithm can both preserve the information of the source images well and suppress pixel distortion due to nonlinear operations in transform domain.Experimental results demonstrate that the proposed method outperforms the state-of-the-art transform domain and pulse coupled neural network (PCNN) fusion methods.