Dian Renwei, Li Shutao
IEEE Trans Image Process. 2019 May 20. doi: 10.1109/TIP.2019.2916734.
Recently, combining a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) into an HR-HSI has become a popular scheme to enhance the spatial resolution of HSI. We propose a novel subspace-based low tensor multi-rank regularization method for the fusion, which fully exploits the spectral correlations and non-local similarities in the HR-HSI. To make use of high spectral correlations, the HR-HSI is approximated by spectral subspace and coefficients. We first learn the spectral subspace from the LR-HSI via singular value decomposition, and then estimate the coefficients via the low tensor multi-rank prior. More specifically, based on the learned cluster structure in the HR-MSI, the patches in coefficients are grouped. We collect the coefficients in the same cluster into a three-dimensional tensor and impose the low tensor multi-rank prior on these collected tensors, which fully model the non-local self-similarities in the HR-HSI. The coefficients optimization is solved by the alternating direction method of multipliers. Experiments on two public HSI datasets demonstrate the advantages of tour method.
最近，将低空间分辨率高光谱图像（LR-HSI）与高空间分辨率多光谱图像（HR-MSI）融合成高分辨率高光谱图像（HR-HSI）已成为提高高光谱图像空间分辨率的一种流行方案。我们提出了一种新颖的基于子空间的低张量多秩正则化融合方法，该方法充分利用了HR-HSI中的光谱相关性和非局部相似性。为了利用高光谱相关性，通过光谱子空间和系数对HR-HSI进行近似。我们首先通过奇异值分解从LR-HSI中学习光谱子空间，然后通过低张量多秩先验估计系数。更具体地说，基于在HR-MSI中学习到的聚类结构，对系数中的补丁进行分组。我们将同一聚类中的系数收集到一个三维张量中，并对这些收集到的张量施加低张量多秩先验，这充分模拟了HR-HSI中的非局部自相似性。系数优化通过乘子交替方向法求解。在两个公共高光谱图像数据集上的实验证明了我们方法的优势。
