论文标题
通过耦合张量环分解高光谱超分辨率
Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization
论文作者
论文摘要
高光谱超分辨率（HSR）融合了低分辨率的高光谱图像（HSI）和高分辨率的多光谱图像（MSI），以获得高分辨率HSI（HR-HSI）。在本文中，我们为HSR提出了一个新模型，称为耦合张量环（CTRF）。所提出的CTRF方法同时从MSI中从HSI和高空间分辨率核心张量中学习了高光谱分辨率核心张量，并通过张量环（TR）表示重建HR-HSI（图〜\ ref Ref {fig {fig：framework}）。 CTRF模型可以单独利用每个类的低级别属性（\ ref {sec：Analysis}），在先前的耦合张量模型中从未探索过。同时，它继承了耦合矩阵/CP分解的简单表示和偶联的塔克分解的柔性低级探索。 在定理〜\ ref {th：1}的指导下，我们进一步提出了光谱核规范正规化，以探索全球频谱低级别的性质。 与以前的矩阵/张量和深度学习方法相比，该实验证明了提出的核规范正规化CTRF（NCTRF）的优势。
Hyperspectral super-resolution (HSR) fuses a low-resolution hyperspectral image (HSI) and a high-resolution multispectral image (MSI) to obtain a high-resolution HSI (HR-HSI). In this paper, we propose a new model, named coupled tensor ring factorization (CTRF), for HSR. The proposed CTRF approach simultaneously learns high spectral resolution core tensor from the HSI and high spatial resolution core tensors from the MSI, and reconstructs the HR-HSI via tensor ring (TR) representation (Figure~\ref{fig:framework}). The CTRF model can separately exploit the low-rank property of each class (Section \ref{sec:analysis}), which has been never explored in the previous coupled tensor model. Meanwhile, it inherits the simple representation of coupled matrix/CP factorization and flexible low-rank exploration of coupled Tucker factorization. Guided by Theorem~\ref{th:1}, we further propose a spectral nuclear norm regularization to explore the global spectral low-rank property. The experiments have demonstrated the advantage of the proposed nuclear norm regularized CTRF (NCTRF) as compared to previous matrix/tensor and deep learning methods.
