针对非负矩阵分解(NMF)算法时间复杂度较高，而投影梯度(Projected Gradient, PG)优化方法可以大幅降低NMF约束优化迭代问题的时间复杂度，提出一种基于改进的投影梯度NMF(IPGNMF)和非采样Contourlet变换(NSCT)相结合的图像融合方法。采用NSCT对已配准的源图像进行多尺度、多方向的分解，将分解后的低频部分作为原始数据，利用IPGNMF得到包含特征基的低通子带系数；高频部分应用了一种基于邻域一致性测度(NHM)的局部自适应融合规则得到各带通方向子带系数。经过NSCT逆变换得到融合图像。实验结果表明，融合结果在主观和客观评价上均优于NSWT方法、IPGNMF方法和NSCT方法。与NSCT法相比，实验所采用的两组图像的信息熵、清晰度和Q指标分别提高了0.062 7%、0.901%、3.120 1%和 2.769%、2.203%、1.049%。

As the Non-negative Matrix Factorization (NMF) algorithm has a higher iteration time complexity and the Gradient Projection(BP) optimization method can significantly reduce the NMF iteration time complexity, an image fusion algorithm by combiing the improved PGNMF and Nonsubsampled Contourlet Transform (NSCT) is proposed in this paper. Firstly, the reistered oriinal images are decomposited in multi-scale and multi-direction decomposition in NSCT domain. According to the characters of the different areas, different fusion rules are designed in the NSCT domain. The low-pass sub band coefficients used as original data impose to the IPGNMF algorithm,to obtain the fused low-pass sub band coefficients and the band-pass directional sub band coefficients impose to the Neighborhood Homogeneous Measurement (NHM) algorithm,to obtain the fusion band-pass directional sub band coefficients. Finally, the fused result is obtained through inverse NSCT. The propose algorithm has been experimented on two groups of different scene images,and experimental results show that it superior to those conventional fusion methods based on NSWT,IPGNMF and NSCT in subjective and objective standards.As contrasted with NSCT method in two groups images, its entropy, definition and QABIF have been increased by 0.0627%、0.901%、3.1201% and 2.769%、2.203%、1.049%,respectively。