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Tensor decompositions for hyperspectral data processing in remote sensing: A comprehensive review
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …
(RS) imaging has provided a significant amount of spatial and spectral information for the …
Non-local meets global: An iterative paradigm for hyperspectral image restoration
Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …
[HTML][HTML] Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2,1 Norm
To improve the detection ability of infrared small targets in complex backgrounds, a novel
method based on non-convex rank approximation minimization joint l 2, 1 norm (NRAM) was …
method based on non-convex rank approximation minimization joint l 2, 1 norm (NRAM) was …
Hyperspectral image restoration via total variation regularized low-rank tensor decomposition
Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise
during the acquisition process, eg, Gaussian noise, impulse noise, dead lines, stripes, etc …
during the acquisition process, eg, Gaussian noise, impulse noise, dead lines, stripes, etc …
Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery
As a promising way for analyzing data, sparse modeling has achieved great success
throughout science and engineering. It is well known that the sparsity/low-rank of a …
throughout science and engineering. It is well known that the sparsity/low-rank of a …
When Laplacian scale mixture meets three-layer transform: A parametric tensor sparsity for tensor completion
Recently, tensor sparsity modeling has achieved great success in the tensor completion
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …
Nonlocal patch tensor sparse representation for hyperspectral image super-resolution
This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a
low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high …
low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high …
Hyperspectral image denoising with total variation regularization and nonlocal low-rank tensor decomposition
Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types,
which degrades the quality of the acquired image and limits the subsequent application. In …
which degrades the quality of the acquired image and limits the subsequent application. In …
Fast noise removal in hyperspectral images via representative coefficient total variation
Mining structural priors in data is a widely recognized technique for hyperspectral image
(HSI) denoising tasks, whose typical ways include model-based methods and data-based …
(HSI) denoising tasks, whose typical ways include model-based methods and data-based …
Nonlocal coupled tensor CP decomposition for hyperspectral and multispectral image fusion
Hyperspectral (HS) super-resolution, which aims at enhancing the spatial resolution of
hyperspectral images (HSIs), has recently attracted considerable attention. A common way …
hyperspectral images (HSIs), has recently attracted considerable attention. A common way …