We propose a novel approach for hyperspectral super-resolution, that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the correct recovery holds for a wide range of multilinear ranks. For coupled tensor approximation, we propose two SVD-based algorithms that are simple and fast, but with a performance comparable to the state-of-the-art methods. The approach is applicable to the case of unknown spatial degradation and to the pansharpening problem.

中文翻译：

---

耦合塔克近似的高光谱超分辨率：可恢复性和基于 SVD 的算法


我们提出了一种新的高光谱超分辨率方法，该方法基于耦合低秩多线性（Tucker）模型的低秩张量近似。我们证明了正确的恢复适用于广泛的多线性等级。对于耦合张量逼近，我们提出了两种基于 SVD 的算法，它们简单而快速，但性能与最先进的方法相当。该方法适用于未知空间退化的情况和全色锐化问题。