Mixed noise removal in hyperspectral image via low-fibered-rank regularization
The tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD),
has obtained promising results in hyperspectral image (HSI) denoising. However, the …
has obtained promising results in hyperspectral image (HSI) denoising. However, the …
A systematic review on recent developments in nonlocal and variational methods for SAR image despeckling
Speckle is a granular deformity that frequently appears in images acquired through coherent
imaging sensors such as synthetic aperture radar (SAR). The existence of such noise in the …
imaging sensors such as synthetic aperture radar (SAR). The existence of such noise in the …
Fastderain: A novel video rain streak removal method using directional gradient priors
Rain streaks removal is an important issue in outdoor vision systems and has recently been
investigated extensively. In this paper, we propose a novel video rain streak removal …
investigated extensively. In this paper, we propose a novel video rain streak removal …
[HTML][HTML] A directional global sparse model for single image rain removal
Rain removal from a single image is an important issue in the fields of outdoor vision. Rain,
a kind of bad weather that is often seen, usually causes complex local intensity changes in …
a kind of bad weather that is often seen, usually causes complex local intensity changes in …
Double-factor-regularized low-rank tensor factorization for mixed noise removal in hyperspectral image
As a preprocessing step, hyperspectral image (HSI) restoration plays a critical role in many
subsequent applications. Recently, based on the framework of subspace representation and …
subsequent applications. Recently, based on the framework of subspace representation and …
Low-rank tensor train for tensor robust principal component analysis
Recently, tensor train rank, defined by a well-balanced matricization scheme, has been
shown the powerful capacity to capture the hidden correlations among different modes of a …
shown the powerful capacity to capture the hidden correlations among different modes of a …
The fusion of panchromatic and multispectral remote sensing images via tensor-based sparse modeling and hyper-Laplacian prior
In this paper, we propose a tensor-based non-convex sparse modeling approach for the
fusion of panchromatic and multispectral remote sensing images, and this kind of fusion is …
fusion of panchromatic and multispectral remote sensing images, and this kind of fusion is …
[HTML][HTML] Remote sensing images destri** using unidirectional hybrid total variation and nonconvex low-rank regularization
In this paper, we propose a novel model for remote sensing images destri**, which
includes the Schatten 1∕ 2-norm and the unidirectional first-order and high-order total …
includes the Schatten 1∕ 2-norm and the unidirectional first-order and high-order total …
Joint-sparse-blocks and low-rank representation for hyperspectral unmixing
J Huang, TZ Huang, LJ Deng… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Hyperspectral unmixing has attracted much attention in recent years. Single sparse
unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number …
unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number …
Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery
The recent popular tensor tubal rank, defined based on tensor singular value decomposition
(t-SVD), yields promising results. However, its framework is applicable only to three-way …
(t-SVD), yields promising results. However, its framework is applicable only to three-way …