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Exploring coupled images fusion based on joint tensor decomposition

Lu, Liangfu; Ren, Xiaoxu; Yeh, Kuo-Hui; Tan, Zhiyuan; Chanussot, Jocelyn


Liangfu Lu

Xiaoxu Ren

Kuo-Hui Yeh

Jocelyn Chanussot


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.


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, Article 10 (2020).

Journal Article Type Article
Acceptance Date Feb 4, 2020
Online Publication Date Mar 27, 2020
Publication Date 2020
Deposit Date Feb 12, 2020
Publicly Available Date Feb 17, 2020
Publisher SpringerOpen
Peer Reviewed Peer Reviewed
Volume 10
Article Number 10 (2020)
Keywords data fusion; coupled image; machine learning; tensor decomposition; AI
Public URL


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