Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction
Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector...
Main Authors: | Jize Xue, Yongqiang Zhao, Wenzhi Liao, Jonathan Cheung-Wai Chan |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | http://www.mdpi.com/2072-4292/11/2/193 |
Similar Items
-
Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition
by: Zhihong Huang, et al.
Published: (2018-01-01) -
Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images
by: Le Sun, et al.
Published: (2020-01-01) -
Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing
by: Le Dong, et al.
Published: (2021-04-01) -
A Low-Rank Tensor Model for Hyperspectral Image Sparse Noise Removal
by: Lizhen Deng, et al.
Published: (2018-01-01) -
Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing
by: Pan Zheng, et al.
Published: (2021-01-01)