摘要翻译：
高光谱超分辨率是指将高光谱图像(HSI)和多光谱图像(MSI)融合生成具有良好空间分辨率和光谱分辨率的超分辨率图像(SRI)。最先进的方法是通过对矩阵化的HSI和MSI的低秩矩阵逼近来解决这个问题。这些方法在一定程度上是有效的，但仍然存在一些挑战。首先，HSIs和MSIs自然是三阶张量（数据“立方体”），因此矩阵化容易丢失结构信息--这可能会降低性能。其次，这些基于低秩矩阵的融合策略是否能保证SRI的可识别性或准确恢复还不清楚。然而，可辨识性在估计问题中起着关键作用，在实际中通常对性能有重大影响。第三，现有的大多数方法都假设存在已知的（或容易估计的）退化算子应用于SRI以形成相应的HSI和MSI--而实际情况几乎不是这样。在本工作中，我们提出从张量的角度来解决超分辨率问题。具体来说，我们利用HSI和MSI的多维结构提出了一个耦合张量分解框架，可以有效地克服上述问题。所提出的方法保证了在温和和现实的条件下，性权利倡议的可识别性。此外，它在对退化算子知之甚少的情况下工作，这显然是现有方法的一个优势。通过半真实数值实验验证了该方法的有效性。
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英文标题：
《Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach》
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作者：
Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos, and
  Wing-Kin Ma
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Hyperspectral super-resolution refers to the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image (SRI) that has fine spatial and spectral resolution. State-of-the-art methods approach the problem via low-rank matrix approximations to the matricized HSI and MSI. These methods are effective to some extent, but a number of challenges remain. First, HSIs and MSIs are naturally third-order tensors (data "cubes") and thus matricization is prone to loss of structural information--which could degrade performance. Second, it is unclear whether or not these low-rank matrix-based fusion strategies can guarantee identifiability or exact recovery of the SRI. However, identifiability plays a pivotal role in estimation problems and usually has a significant impact on performance in practice. Third, the majority of the existing methods assume that there are known (or easily estimated) degradation operators applied to the SRI to form the corresponding HSI and MSI--which is hardly the case in practice. In this work, we propose to tackle the super-resolution problem from a tensor perspective. Specifically, we utilize the multidimensional structure of the HSI and MSI to propose a coupled tensor factorization framework that can effectively overcome the aforementioned issues. The proposed approach guarantees the identifiability of the SRI under mild and realistic conditions. Furthermore, it works with little knowledge of the degradation operators, which is clearly an advantage over the existing methods. Semi-real numerical experiments are included to show the effectiveness of the proposed approach.
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PDF链接：
https://arxiv.org/pdf/1804.05307