耦合图像间的数据融合算法

耦合数据的融合算法主要通过利用其中一个数据集的信息提高对其他耦合数据集的估计精度和完善对相关潜变量的解释。针对现实中存在的大量耦合图像，基于耦合数据融合中的耦合矩阵和张量分解优化（CMTF-OPT）算法，提出一种针对耦合图像的耦合图像分解优化（CIF-OPT）算法。相应的理论分析及实验结果表明，不同噪声影响下用CIF-OPT算法进行耦合图像融合后的效果均具有鲁棒性，且融合效果优于其他耦合算法（如：CMTF-OPT算法）。特别地，针对其中缺失数据元素的图像，CIF-OPT算法可以利用与其耦合的图像，对缺失数据元素的图像进行精确的数据恢复。

Data fusion algorithm of coupled images

Coupled data fusion algorithms mainly improve estimation accuracy and explain related latent variables of the other coupled data sets by using the information of one data set. Aiming at the large number of coupled images existing in reality, based on the Coupled Matrix and Tensor Factorization-OPTimization (CMTF-OPT) algorithm in coupled data fusion, a Coupled Images Factorization-OPTimization (CIF-OPT) algorithm for coupled images was proposed. The corresponding theoretical analysis and experimental results show that the effects of coupled image fusion by CIF-OPT algorithm under different noise influence are robust, and better than those by other coupling algorithms. Particularly, the CIF-OPT algorithm can accurately restore an image of missing some data elements with its coupled image at a certain probability.