Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution

Renwei Dian; Shutao Li; Leyuan Fang

首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution

【24h】

Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution

机译：学习低张量火车等级表示法以获得高光谱图像超分辨率

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

Hyperspectral images (HSIs) with high spectral resolution only have the low spatial resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution can be obtained with higher spatial resolution. Therefore, fusing the high-spa

机译：具有高光谱分辨率的高光谱图像（HSI）仅具有较低的空间分辨率。相反，可以以较高的空间分辨率获得光谱分辨率低得多的多光谱图像（MSI）。因此，融合高水疗

著录项

来源
《Neural Networks and Learning Systems, IEEE Transactions on》 |2019年第9期|2672-2683|共12页
作者
Renwei Dian; Shutao Li; Leyuan Fang;
展开▼
作者单位

College of Electrical and Information Engineering Hunan University Changsha China;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类
关键词
Spatial resolution; Dictionaries; Correlation; Hyperspectral imaging; Matrix decomposition;

机译：空间分辨率;字典;相关性高光谱成像;矩阵分解;

相似文献

外文文献
中文文献
专利

1. Single image super-resolution via low-rank tensor representation and hierarchical dictionary learning [J] . Peiguang Jing, Weili Guan, Xu Bai, Multimedia Tools and Applications . 2020,第17a18期

机译：单图像超分辨率通过低级张量表示和分层字典学习
2. Spatial–Spectral Locality-Constrained Low-Rank Representation with Semi-Supervised Hypergraph Learning for Hyperspectral Image Classification [J] . Qingshan Liu, Yubao Sun, Renlong Hang, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of . 2017,第9期

机译：空间谱局部性约束的低秩表示与半监督超图学习的高光谱图像分类
3. Learning group-based sparse and low-rank representation for hyperspectral image classification [J] . He Zhi, Liu Lin, Zhou Suhong, Pattern Recognition: The Journal of the Pattern Recognition Society . 2016,第Null期

机译：基于学习组的稀疏和低秩表示用于高光谱图像分类
4. Relaxed Low Tensor Train Rank Representation with Structural Smoothness for Hyperspectral Image Super-resolution [C] . Shengchuan Li, Huidi Jia, Xiai Chen, IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems . 2020

机译：轻松的低张力列车排名表示，具有高光谱图像超分辨率的结构光滑度
5. Low Rank Models for Multi-Dimensional Data Recovery and Image Super-Resolution [D] . Al-Qizwini, Mohammed. 2017

机译：用于多维数据恢复和图像超分辨率的低秩模型
6. A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images [O] . Yi Zhang, Zebin Wu, Jin Sun, 2018

机译：基于低秩稀疏表示的分布式并行高光谱图像异常检测算法
7. Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization [O] . He, Shiying, Zhou, Haiwei, Wang Y(王尧), 2016

机译：通过低秩张量建模和总变化正则化超光谱图像的超分辨率重建

Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution

摘要

著录项

相似文献

相关主题

期刊订阅