在标准非负矩阵分解约束条件的基础上,提出了一种添加了清晰度约束的新的目标函数和迭代算法,即改进的非负矩阵分解算法,并将其应用于多聚焦图像融合中。非负矩阵分解过程中,适当地选取特征空间的维数能够获得原始数据的局部特征。若以待融合图像为原始数据,选取特征空间的维数为1,则利用改进的非负矩阵分解方法进行图像融合所得到的特征基图像就是对原始图像的融合,该融合图像包含了原始图像的整体特征。实验结果表明,该方法融合效果优于小波变换方法和拉普拉斯塔型方法。

A new method based on the sharpness-constrained non-negative matrix factorization (SNMF) technique was presented for multi-focus images fusion. An new objective function was defined to impose sharpness constraint, in addition to the non-negativity constraint in the standard NMF. An algorithm was presented for SNMF. The local feature based representation could be obtained by choosing suitable dimension of the feature subspace in NMF. It was pointed out that when using SNMF, if the dimension of the feature subspace was set to 1, the resulted feature base was just the fusion result of the original input images. The feature base obtained included the global feature of the original images. Experimental results were presented to compare SNMF with wavelet transform and Laplacian methods for image fusion, which demonstrates advantages of SNMF in preserving the global feature information.