IEEE Trans Image Process. 2016 May;25(5):2337-52. doi: 10.1109/TIP.2016.2542360.
Hyperspectral imaging has many applications from agriculture and astronomy to surveillance and mineralogy. However, it is often challenging to obtain high-resolution (HR) hyperspectral images using existing hyperspectral imaging techniques due to various hardware limitations. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene. The estimation of the HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the prior knowledge of the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary representing prototype reflectance spectra vectors of the scene is first learned from the input LR image. Specifically, an efficient non-negative dictionary learning algorithm using the block-coordinate descent optimization technique is proposed. Then, the sparse codes of the desired HR hyperspectral image with respect to learned hyperspectral basis are estimated from the pair of LR and HR reference images. To improve the accuracy of non-negative sparse coding, a clustering-based structured sparse coding method is proposed to exploit the spatial correlation among the learned sparse codes. The experimental results on both public datasets and real LR hypspectral images suggest that the proposed method substantially outperforms several existing HR hyperspectral image recovery techniques in the literature in terms of both objective quality metrics and computational efficiency.
高光谱成像是一种应用广泛的技术，涵盖农业、天文学、监控和矿物学等领域。然而，由于各种硬件限制，利用现有的高光谱成像技术获取高分辨率（HR）高光谱图像通常具有挑战性。在本文中，我们提出了一种新的高光谱图像超分辨率方法，该方法基于同一场景的低分辨率（LR）图像和高分辨率参考图像。HR 高光谱图像的估计被表述为基于高光谱图像的空间-光谱稀疏性先验知识的高光谱字典和稀疏码的联合估计。表示场景原型反射光谱向量的高光谱字典首先从输入的 LR 图像中学习。具体来说，提出了一种利用块坐标下降优化技术的高效非负字典学习算法。然后，从 LR 和 HR 参考图像对中估计出期望的 HR 高光谱图像相对于学习到的高光谱基的稀疏码。为了提高非负稀疏编码的准确性，提出了一种基于聚类的结构稀疏编码方法，以利用学到的稀疏码之间的空间相关性。在公共数据集和真实的 LR 高光谱图像上的实验结果表明，与文献中的几种现有的 HR 高光谱图像恢复技术相比，该方法在客观质量指标和计算效率方面都有显著的提高。
