Hyperspectral image super-resolution via subspace-based fast low tensor multi-rank regularization

The demand for acquiring a high-spatial-resolution hyperspectral image (HR-HSI) imposes challenges to existing hyperspectral imaging tasks. Generally, fusing a high-spatial-resolution multispectral image (HR-MSI) with a low-spatial-resolution hyperspectral image (LR-HSI) is likely the most viable path towards HR-HSI. The Low Tensor Multi-Rank (LTMR) method is demonstrated a good fusion performance. However, it takes a long execution time. To rectify this problem, we propose a fast low tensor multi-rank (FLTMR) hyperspectral super-resolution method to improve the performance in three aspects. To speed up the estimation of spectral coefficients, a set of characteristic values representing HR-HSI are extracted from LR-HSI as the truncation value of tensor reconstruction. To further optimize the calculation time of the algorithm, a truncated value weight coefficient is introduced. Furthermore, applying the iterative attenuation coefficient to the iterative solution can accelerate and achieve better coefficient estimation results. Consequently, our FLTMR approach alleviates the excellent fusion results' requirements and enables rapid processing of the fusion. It can be illustrated that our algorithm consumes only 1/4 to 2/3 of the time while achieves better or similar performance to LTMR. Essentially, owing to our proposed method's excellent performance in the coefficient tensor reconstruction, our method may have important significance for the study of tensor reconstruction.

Automated summary

The article introduces a fast low tensor multi-rank (FLTMR) hyperspectral super-resolution method, which optimizes the estimation of spectral coefficients, computation time of the algorithm, and applies iterative attenuation coefficients to accelerate and achieve better estimation results. The proposed method shows efficient fusion of LR-HSI and HR-MSI, consuming less time while achieving improved performance.