Artificial intelligence in image reconstruction: the change is here

R Singh, W Wu, G Wang, MK Kalra - Physica Medica, 2020 - Elsevier
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 …

Tensor robust principal component analysis with a new tensor nuclear norm

C Lu, J Feng, Y Chen, W Liu, Z Lin… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
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 …

Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization

C Lu, J Feng, Y Chen, W Liu, Z Lin… - Proceedings of the IEEE …, 2016 - cv-foundation.org
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 …

Exact tensor completion using t-SVD

Z Zhang, S Aeron - IEEE Transactions on Signal Processing, 2016 - ieeexplore.ieee.org
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 …

Hyperspectral image super-resolution via subspace-based low tensor multi-rank regularization

R Dian, S Li - IEEE Transactions on Image Processing, 2019 - ieeexplore.ieee.org
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 …

On unifying multi-view self-representations for clustering by tensor multi-rank minimization

Y Xie, D Tao, W Zhang, Y Liu, L Zhang, Y Qu - International Journal of …, 2018 - Springer
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 …

Tensor factorization for low-rank tensor completion

P Zhou, C Lu, Z Lin, C Zhang - IEEE Transactions on Image …, 2017 - ieeexplore.ieee.org
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 …

Low-rank high-order tensor completion with applications in visual data

W Qin, H Wang, F Zhang, J Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion
(LRTC) has achieved unprecedented success in addressing various pattern analysis issues …

Nonlocal patch tensor sparse representation for hyperspectral image super-resolution

Y Xu, Z Wu, J Chanussot, Z Wei - IEEE Transactions on Image …, 2019 - ieeexplore.ieee.org
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 …

Tensor completion via complementary global, local, and nonlocal priors

XL Zhao, JH Yang, TH Ma, TX Jiang… - … on Image Processing, 2021 - ieeexplore.ieee.org
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 …