Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring Decomposition
The fusion of a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI) is an effective way to generate a high-resolution hyperspectral image (HR-HSI). In recent years, methods based on tensor ring (TR) decomposition have received widespread att...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11048944/ |
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| author | Jun Zhang Mengling He Chengzhi Deng |
| author_facet | Jun Zhang Mengling He Chengzhi Deng |
| author_sort | Jun Zhang |
| collection | DOAJ |
| description | The fusion of a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI) is an effective way to generate a high-resolution hyperspectral image (HR-HSI). In recent years, methods based on tensor ring (TR) decomposition have received widespread attention due to their superior performance in approximating high-dimensional data. However, these methods often neglect the intrinsic low-rank property of TR factors. More importantly, even with low-rank consideration, their effectiveness remains severely limited by both the restrictive low-rank tensor definition and high sensitivity to the permutation of tensor modes, ultimately degrading their performance. To address these issues, we propose a new HSI–MSI fusion model based on the generalized logarithmic tensor nuclear norm (GLTNN) under the TR decomposition framework. Specifically, we extend the traditional LTNN based on the third pattern to any pattern and define the generalized LTNN, where the Fourier transform is conducted on arbitrary mode. This method can not only capture the correlations comprehensively for tensor modes, but also effectively avoid the influence of the permutation of tensor modes on the fusion results. In addition, we design a proximal alternating minimization algorithm to efficiently solve the proposed model. The experimental results on four public datasets show that our method outperforms existing approaches in both numerical metrics and visual quality, validating its effectiveness and superiority. |
| format | Article |
| id | doaj-art-8ecdf1b14cee4552bb3b07ba6c4f98b0 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-8ecdf1b14cee4552bb3b07ba6c4f98b02025-08-20T03:27:26ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118165961660810.1109/JSTARS.2025.358278211048944Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring DecompositionJun Zhang0https://orcid.org/0000-0003-3809-7023Mengling He1Chengzhi Deng2https://orcid.org/0000-0003-1605-7100College of Science & Key Laboratory of Engineering Mathematics and Advanced Computing, Jiangxi University of Water Resources and Electric Power, Nanchang, ChinaCollege of Science, Jiangxi University of Water Resources and Electric Power, Nanchang, ChinaJiangxi Province Key Laboratory of Smart Water Conservancy, Jiangxi University of Water Resources and Electric Power, Nanchang, ChinaThe fusion of a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI) is an effective way to generate a high-resolution hyperspectral image (HR-HSI). In recent years, methods based on tensor ring (TR) decomposition have received widespread attention due to their superior performance in approximating high-dimensional data. However, these methods often neglect the intrinsic low-rank property of TR factors. More importantly, even with low-rank consideration, their effectiveness remains severely limited by both the restrictive low-rank tensor definition and high sensitivity to the permutation of tensor modes, ultimately degrading their performance. To address these issues, we propose a new HSI–MSI fusion model based on the generalized logarithmic tensor nuclear norm (GLTNN) under the TR decomposition framework. Specifically, we extend the traditional LTNN based on the third pattern to any pattern and define the generalized LTNN, where the Fourier transform is conducted on arbitrary mode. This method can not only capture the correlations comprehensively for tensor modes, but also effectively avoid the influence of the permutation of tensor modes on the fusion results. In addition, we design a proximal alternating minimization algorithm to efficiently solve the proposed model. The experimental results on four public datasets show that our method outperforms existing approaches in both numerical metrics and visual quality, validating its effectiveness and superiority.https://ieeexplore.ieee.org/document/11048944/Generalized logarithmic tensor nuclear norm (GLTNN)hyperspectral and multispectral image fusionproximal alternating minimization (PAM)tensor ring (TR) decomposition |
| spellingShingle | Jun Zhang Mengling He Chengzhi Deng Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring Decomposition IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Generalized logarithmic tensor nuclear norm (GLTNN) hyperspectral and multispectral image fusion proximal alternating minimization (PAM) tensor ring (TR) decomposition |
| title | Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring Decomposition |
| title_full | Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring Decomposition |
| title_fullStr | Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring Decomposition |
| title_full_unstemmed | Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring Decomposition |
| title_short | Generalized Logarithmic Tensor Nuclear Norm for Hyperspectral-Multispectral Image Fusion via Tensor Ring Decomposition |
| title_sort | generalized logarithmic tensor nuclear norm for hyperspectral multispectral image fusion via tensor ring decomposition |
| topic | Generalized logarithmic tensor nuclear norm (GLTNN) hyperspectral and multispectral image fusion proximal alternating minimization (PAM) tensor ring (TR) decomposition |
| url | https://ieeexplore.ieee.org/document/11048944/ |
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