Smooth Coupled Tucker Decomposition for Hyperspectral Image Super-Resolution.
2021
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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