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Transferable Multiple Subspace Learning for Hyperspectral Image Super-Resolution
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DOI：10.1109/LGRS.2023.3339505.png)
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摘要
En 中文
In real hyperspectral scenes, heterogeneous spatial details and noises make a single subspace assumptions unrealistic. In this letter, a novel transferable multiple tensor subspace learning scheme is proposed for super-resolution enhancement of hyperspectral image (HSI). The intrinsic assumption is that the nonlocal patch tensors extracted from HSIs are derived from multiple tensor low-rank subspaces, which is compatible with practical data distribution and may better characterize the complex structures underlying HSIs. The transferable subspace structures are embedded into both nonblind and semi-blind HSI super-resolution. The alternating direction method of multipliers (ADMMs) algorithm is derived for model learning. The superiority of our method is demonstrated by comprehensive experiments on both synthetic and real datasets.
Keyword:
Tensors
Superresolution
Spatial resolution
Hyperspectral imaging
Dictionaries
Optimization
Learning systems
low-rankness
super-resolution
tensor subspace representation
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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