Hyperspectral super-resolution via coupled tensor ring factorization
作者：
Highlights：
• Based on TR factorization, we developed a degradation model from the HR-HSI to the MSI and HSI. We proposed a CTRF model for HSR tasks. The nuclear norm regularization of the third TR core with mode-2 unfolding was introduced to further exploit the global spectral low-rank property of the HR-HSI.
• We analyzed the superiority of the CTRF model for HSR and developed an efficient alternating iteration method for the proposed model. The experiments demonstrated the advantage of the CTRF model compared to the previous matrix/tensor and deep learning methods.
摘要
•Based on TR factorization, we developed a degradation model from the HR-HSI to the MSI and HSI. We proposed a CTRF model for HSR tasks. The nuclear norm regularization of the third TR core with mode-2 unfolding was introduced to further exploit the global spectral low-rank property of the HR-HSI.•We analyzed the superiority of the CTRF model for HSR and developed an efficient alternating iteration method for the proposed model. The experiments demonstrated the advantage of the CTRF model compared to the previous matrix/tensor and deep learning methods.
论文关键词：Coupled tensor ring decomposition,Super-resolution,Hyperspectral,Multispectral
论文评审过程：Received 24 February 2020, Revised 15 March 2021, Accepted 24 August 2021, Available online 26 August 2021, Version of Record 3 September 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108280
