基于稀疏性非负矩阵分解的偏振图像快速融合方法

针对基于非负矩阵分解(non-negative matrix factorization, NMF)的偏振图像融合方法效率低的不足，提出一种基于稀疏性NMF的偏振图像快速融合方法。首先，以偏振信息解析得到的各偏振参量图像构造原始数据集，其次，对NMF增加稀疏性约束，利用稀疏表示下的在线字典学习算法进行快速分解，然后对分解得到的三幅特征基图像按清晰度和方差进行排序，将排序后的特征基图像经直方图匹配及HSI颜色映射后，变换到RGB颜色空间，得到融合图像。与基于NMF的方法相比，运行时间提高约120倍，达到约1.5 s完成一次融合过程。实验结果验证了该方法在改善融合效果的同时，运行效率明显提高。

Polarimetric Image Fast Fusion Method Via Sparse Non-Negative Matrix Factorization

To improve the efficiency of the polarimetric image fusion methods via NMF, a fast fusion method based on sparse NMF was proposed. Firstly, the polarization parameter images were acquired by computing from intensity images of different polarization angle. The original data set was organized by the polarization parameter images. Secondly, the sparse constraint was added to NMF and the cost function was solved by online dictionary learning algorithm of sparse representation. Then, the data set was factorized by sparse NMF and three feature basis images sorted by definition and variance were obtained. Next, after histogram matching, these three sorted feature basis images were mapped into three color channels of HSI color model. Finally, the fused image was achieved by transforming the image from HSI to RGB color model. Compared with the method based on NMF, the running time is improved with 120 times. One fusing process can be finished in 1.5 s. Experiment results show that the proposed method not only has good fusion results but also enhances the running efficiency evidently.