Multimodal Low-Rank Tensor Subspace Learning for Hyperspectral Image Restoration

The restoration of hyperspectral images (HSIs) is a crucial process that eliminates various types of noise to improve subsequent applications. To effectively utilize the inherent low-rank and spatial smoothness of HSI data, this letter proposes a method that employs Multimodal Low-rank Tensor Subspa...

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Vydané v:IEEE geoscience and remote sensing letters Ročník 20; s. 1
Hlavní autori: Lv, Junrui, Luo, Xuegang, Wang, Juan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Hyperspectral Image Restoration
Hyperspectral imaging
Image restoration
Indexes
Learning
Low-rankness
Mathematical analysis
Minimization
Multimodal Tensor factorization
Noise reduction
Optimization
Regularization
Restoration
Smoothness
Subspace Learning
Subspaces
Tensors
Transforms
ISSN:1545-598X, 1558-0571
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Shrnutí:The restoration of hyperspectral images (HSIs) is a crucial process that eliminates various types of noise to improve subsequent applications. To effectively utilize the inherent low-rank and spatial smoothness of HSI data, this letter proposes a method that employs Multimodal Low-rank Tensor Subspace Learning with Total Variation regularization (MLTSL-TV) model to denoise HSI data based on observed measurements. The proposed approach utilizes a low-rankness measure of subspace tensors and learnable transform basis to represent a low-rank perspective, which enables adaptive exploitation of potential low-rank structures through multi-modal tensor factorization in multiple orientations based on the observed HSI data. More importantly, we put forward a proximal alternating minimization (PAM) algorithm for efficiently solving the proposed model. Experiments were conducted on two simulated and one real HSI dataset, which were compared with representative approaches through both visual and quantitative analysis. The experimental results demonstrate that the proposed MLTSL-TV approach achieves satisfactory performance when compared to state-of-the-art methods.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3301865