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Abstract
Recently, it has become popular to obtain a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution RGB image (HR-RGB). Existing HSI super-resolution methods are designed based on a known spatial degeneration. In practice, it is difficult to obtain correct spatial degradation, which restricts the performance of existing methods. Therefore, we propose a multi-stage scheme without employing the spatial degradation model. The multi-stage scheme consists of three stages: initialization, modification, and refinement. According to the angle similarity between the HR-RGB pixel and LR-HSI spectra, we first initialize a spectrum for each HR-RGB pixel. Then, we propose a polynomial function to modify the initialized spectrum so that the RGB color values of the modified spectrum are the same as the HR-RGB. Finally, the modified HR-HSI is refined by a proposed optimization model, in which a novel, to the best of our knowledge, spectral-spatial total variation (SSTV) regularizer is investigated to keep the spectral and spatial structure of the reconstructed HR-HSI. The experimental results on two public datasets and our real-world images demonstrate our method outperforms eight state-of-the-art existing methods in terms of both reconstruction accuracy and computational efficiency.
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