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Multilayer sparsity-based tensor decomposition for low-rank tensor completion
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …
Classification via structure-preserved hypergraph convolution network for hyperspectral image
Graph convolutional network (GCN) as a combination of deep learning (DL) and graph
learning has gained increasing attention in hyperspectral image (HSI) classification …
learning has gained increasing attention in hyperspectral image (HSI) classification …
Spatial-spectral structured sparse low-rank representation for hyperspectral image super-resolution
Hyperspectral image super-resolution by fusing high-resolution multispectral image (HR-
MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution …
MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution …
Hyperspectral image compressive sensing reconstruction using subspace-based nonlocal tensor ring decomposition
Hyperspectral image compressive sensing reconstruction (HSI-CSR) can largely reduce the
high expense and low efficiency of transmitting HSI to ground stations by storing a few …
high expense and low efficiency of transmitting HSI to ground stations by storing a few …
Tensor cascaded-rank minimization in subspace: A unified regime for hyperspectral image low-level vision
Low-rank tensor representation philosophy has enjoyed a reputation in many hyperspectral
image (HSI) low-level vision applications, but previous studies often failed to …
image (HSI) low-level vision applications, but previous studies often failed to …
HI-GAN: A hierarchical generative adversarial network for blind denoising of real photographs
Although deep convolutional neural networks (DCNNs) and generative adversarial networks
(GANs) have achieved remarkable success in image denoising, they have been facing a …
(GANs) have achieved remarkable success in image denoising, they have been facing a …
Hyperspectral Image Restoration via Global L1-2 Spatial–Spectral Total Variation Regularized Local Low-Rank Tensor Recovery
H Zeng, X **
Hyperspectral images (HSIs) are inevitably degraded by a mixture of various types of noise,
such as Gaussian noise, impulse noise, stripe noise, and dead pixels, which greatly limits …
such as Gaussian noise, impulse noise, stripe noise, and dead pixels, which greatly limits …
Nonlocal B-spline representation of tensor decomposition for hyperspectral image inpainting
Hyperspectral image (HSI) completion is a fundamental problem in image processing and
remote sensing. Typical methods, either perform suboptimally due to lack of appropriate …
remote sensing. Typical methods, either perform suboptimally due to lack of appropriate …