Affiliation:
1. National Institute of Metrology
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.
Funder
National Natural Science Foundation of China
Beijing Institute of Graphic Communication
Subject
Atomic and Molecular Physics, and Optics
Cited by
2 articles.
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