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 …
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 …
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 …
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 …
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 …
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 …
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 …
Nonconvex tensor low-rank approximation for infrared small target detection
Infrared small target detection is an important fundamental task in the infrared system.
Therefore, many infrared small target detection methods have been proposed, in which the …
Therefore, many infrared small target detection methods have been proposed, in which the …
Low-rank tensor regularized views recovery for incomplete multiview clustering
In real applications, it is often that the collected multiview data contain missing views. Most
existing incomplete multiview clustering (IMVC) methods cannot fully utilize the underlying …
existing incomplete multiview clustering (IMVC) methods cannot fully utilize the underlying …