Low rank tensor completion for multiway visual data
Tensor completion recovers missing entries of multiway data. The missing of entries could
often be caused during the data acquisition and transformation. In this paper, we provide an …
often be caused during the data acquisition and transformation. In this paper, we provide an …
Hyperspectral image super-resolution via deep spatiospectral attention convolutional neural networks
Hyperspectral images (HSIs) are of crucial importance in order to better understand features
from a large number of spectral channels. Restricted by its inner imaging mechanism, the …
from a large number of spectral channels. Restricted by its inner imaging mechanism, the …
Learning a low tensor-train rank representation for hyperspectral image super-resolution
Hyperspectral images (HSIs) with high spectral resolution only have the low spatial
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution …
Tensor methods in computer vision and deep learning
Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …
Fusformer: A transformer-based fusion network for hyperspectral image super-resolution
Hyperspectral image super-resolution (HISR) is to fuse a low-resolution hyperspectral image
(LR-HSI) and a high-resolution multispectral image (HR-MSI), aiming to obtain a high …
(LR-HSI) and a high-resolution multispectral image (HR-MSI), aiming to obtain a high …
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 …
Essential tensor learning for multi-view spectral clustering
Recently, multi-view clustering attracts much attention, which aims to take advantage of multi-
view information to improve the performance of clustering. However, most recent work …
view information to improve the performance of clustering. However, most recent work …
Tensor-SVD based graph learning for multi-view subspace clustering
Low-rank representation based on tensor-Singular Value Decomposition (t-SVD) has
achieved impressive results for multi-view subspace clustering, but it does not well deal with …
achieved impressive results for multi-view subspace clustering, but it does not well deal with …
Framelet representation of tensor nuclear norm for third-order tensor completion
The main aim of this paper is to develop a framelet representation of the tensor nuclear norm
for third-order tensor recovery. In the literature, the tensor nuclear norm can be computed by …
for third-order tensor recovery. In the literature, the tensor nuclear norm can be computed by …
Multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix
Multi-view subspace clustering aims at separating data points into multiple underlying
subspaces according to their multi-view features. Existing low-rank tensor representation …
subspaces according to their multi-view features. Existing low-rank tensor representation …