Artificial intelligence in image reconstruction: the change is here
Innovations in CT have been impressive among imaging and medical technologies in both
the hardware and software domain. The range and speed of CT scanning improved from the …
the hardware and software domain. The range and speed of CT scanning improved from the …
Tensor robust principal component analysis with a new tensor nuclear norm
In this paper, we consider the Tensor Robust Principal Component Analysis (TRPCA)
problem, which aims to exactly recover the low-rank and sparse components from their sum …
problem, which aims to exactly recover the low-rank and sparse components from their sum …
Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization
This paper studies the Tensor Robust Principal Component (TRPCA) problem which
extends the known Robust PCA to the tensor case. Our model is based on a new tensor …
extends the known Robust PCA to the tensor case. Our model is based on a new tensor …
Exact tensor completion using t-SVD
In this paper, we focus on the problem of completion of multidimensional arrays (also
referred to as tensors), in particular three-dimensional (3-D) arrays, from limited sampling …
referred to as tensors), in particular three-dimensional (3-D) arrays, from limited sampling …
Hyperspectral image super-resolution via subspace-based low tensor multi-rank regularization
Recently, combining a low spatial resolution hyperspectral image (LR-HSI) with a high
spatial resolution multispectral image (HR-MSI) into an HR-HSI has become a popular …
spatial resolution multispectral image (HR-MSI) into an HR-HSI has become a popular …
On unifying multi-view self-representations for clustering by tensor multi-rank minimization
In this paper, we address the multi-view subspace clustering problem. Our method utilizes
the circulant algebra for tensor, which is constructed by stacking the subspace …
the circulant algebra for tensor, which is constructed by stacking the subspace …
Tensor factorization for low-rank tensor completion
Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor
completion problem, which has achieved state-of-the-art performance on image and video …
completion problem, which has achieved state-of-the-art performance on image and video …
Low-rank high-order tensor completion with applications in visual data
Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion
(LRTC) has achieved unprecedented success in addressing various pattern analysis issues …
(LRTC) has achieved unprecedented success in addressing various pattern analysis issues …
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 …
Tensor completion via complementary global, local, and nonlocal priors
Completing missing entries in multidimensional visual data is a typical ill-posed problem that
requires appropriate exploitation of prior information of the underlying data. Commonly used …
requires appropriate exploitation of prior information of the underlying data. Commonly used …