Research Output
Exploring coupled images fusion based on joint tensor decomposition
  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.

  • Type:


  • Date:

    27 March 2020

  • Publication Status:


  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    National Natural Science Foundation of China


Lu, L., Ren, X., Yeh, K., Tan, Z., & Chanussot, J. (2020). Exploring coupled images fusion based on joint tensor decomposition. Human-Centric Computing and Information Sciences, 10,



data fusion; coupled image; machine learning; tensor decomposition; AI

Monthly Views:

Available Documents