Abstract
Hyperspectral image processing methods based on Tucker decomposition by utilizing low-rank and sparse priors are sensitive to the model order, and merely utilizing the global structural information. After statistical analysis on hyperspectral images, we find that the smoothness underlying hyperspectral image encoding local structural information is ubiquity in each mode. Based on this observation, we propose a novel smooth coupled Tucker decomposition scheme with two smoothness constraints imposed on the subspace factor matrices to reveal the local structural information of hyperspectral image. In addition, efficient algorithms are designed and experimental results demonstrate the effectiveness of selecting optimal model order for hyperspectral image super-resolution due to the integration of the subspace smoothness.
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Notes
- 1.
In this study, the rank of CPD/Tucker decomposition and the size of dictionaries in [4] are collectively referred as model order. In our models, the size of core tensor is model order.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61371152 and Grant 61771391, in part by the Shenzhen Municipal Science and Technology Innovation Committee under Grant JCYJ20170815162956949 and JCYJ20180306171146740 and in part by Key R&D Plan of Shaanxi Province 2020ZDLGY07–11, by Natural Science Basic Research plan in Shaanxi Province of China 2018JM6056 and by the innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University CX2021081.
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Bu, Y., Zhao, Y., Xue, J., Chan, J.CW. (2021). Smooth Coupled Tucker Decomposition for Hyperspectral Image Super-Resolution. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_20
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