Data fusion has always been a hot research topic in human-centric computing and extended with the development of artificial intelligence. Generally, the coupled data fusion algorithm usually utilizes the information from one data set to improve the estimation accuracy and explain related latent variables of other coupled datasets. This paper proposes several kinds of coupled images decomposition algorithms based on the coupled matrix and tensor factorization-optimization (CMTF-OPT) algorithm and the flexible coupling algorithm, which are termed the coupled images factorization-optimization (CIF-OPT) algorithm and the modified flexible coupling algorithm respectively. The theory and experiments show that the effect of the CIF-OPT algorithm is robust under the influence of different noises. Particularly, the CIF-OPT algorithm can accurately restore an image with missing some data elements. Moreover, the flexible coupling model has better estimation performance than a hard coupling. For high-dimensional images, this paper adopts the compressed data decomposition algorithm that not only works better than uncoupled ALS algorithm as the image noise level increases, but saves time and cost compared to the uncompressed algorithm.

数据融合一直是以人为中心的计算领域的研究热点，并随着人工智能的发展而得到延伸。一般来说，耦合数据融合算法通常利用一个数据集的信息来提高估计精度并解释其他耦合数据集的相关潜在变量。本文提出了几种基于耦合矩阵和张量分解优化（CMTF-OPT）算法和柔性耦合算法的耦合图像分解算法，分别称为耦合图像分解优化（CIF-OPT）算法和改进的耦合图像分解优化算法。分别采用柔性耦合算法。理论和实验表明，CIF-OPT算法在不同噪声的影响下效果具有鲁棒性。特别地，CIF-OPT算法可以准确地恢复丢失某些数据元素的图像。此外，柔性耦合模型比硬耦合模型具有更好的估计性能。对于高维图像，本文采用压缩数据分解算法，该算法不仅随着图像噪声水平的增加比非耦合ALS算法效果更好，而且与非压缩算法相比节省了时间和成本。