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 …
Multiview subspace clustering by an enhanced tensor nuclear norm
Despite the promising preliminary results, tensor-singular value decomposition (t-SVD)-
based multiview subspace is incapable of dealing with real problems, such as noise and …
based multiview subspace is incapable of dealing with real problems, such as noise and …
[HTML][HTML] Generalized tensor function via the tensor singular value decomposition based on the T-product
In this paper, we present the definition of generalized tensor function according to the tensor
singular value decomposition (T-SVD) based on the tensor T-product. Also, we introduce the …
singular value decomposition (T-SVD) based on the tensor T-product. Also, we introduce the …
Sparse regularization-based spatial–temporal twist tensor model for infrared small target detection
J Li, P Zhang, L Zhang, Z Zhang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Infrared (IR) small target detection under complex environments is an essential part of IR
search and track systems. However, previously proposed IR small target detection …
search and track systems. However, previously proposed IR small target detection …
Tensor compressive sensing fused low-rankness and local-smoothness
A plethora of previous studies indicates that making full use of multifarious intrinsic
properties of primordial data is a valid pathway to recover original images from their …
properties of primordial data is a valid pathway to recover original images from their …
Enhanced sparsity prior model for low-rank tensor completion
Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-
valued data lies in the global subspace. The so-called global sparsity prior is measured by …
valued data lies in the global subspace. The so-called global sparsity prior is measured by …
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 …
Denoising of low-dose CT images via low-rank tensor modeling and total variation regularization
Abstract Low-dose Computed Tomography (CT) imaging is a most commonly used medical
imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads …
imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads …
T-positive semidefiniteness of third-order symmetric tensors and T-semidefinite programming
MM Zheng, ZH Huang, Y Wang - Computational Optimization and …, 2021 - Springer
The T-product for third-order tensors has been used extensively in the literature. In this
paper, we first introduce first-order and second-order T-derivatives for the multi-variable real …
paper, we first introduce first-order and second-order T-derivatives for the multi-variable real …
Weighted tensor nuclear norm minimization for tensor completion using tensor-SVD
In this paper, we consider the tensor completion problem, which aims to estimate missing
values from limited information. Our model is based on the recently proposed tensor-SVD …
values from limited information. Our model is based on the recently proposed tensor-SVD …