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Found 26 papers, 3 papers with code
Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent
This paper considers the problem of recovering a tensor with an underlying low-tubal-rank structure from a small number of corrupted linear measurements.
SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective
To address this issue, we propose a novel TN paradigm, named SVD-inspired TN decomposition (SVDinsTN), which allows us to efficiently solve the TN-SS problem from a regularized modeling perspective, eliminating the repeated structure evaluations.
Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing
However, existing LL1-based HU algorithms use a three-factor parameterization of the tensor (i. e., the hyperspectral image cube), which leads to a number of challenges including high per-iteration complexity, slow convergence, and difficulties in incorporating structural prior information.
Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting
Modern time series datasets are often high-dimensional, incomplete/sparse, and nonstationary.
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging
Inverse problems in multi-dimensional imaging, e. g., completion, denoising, and compressive sensing, are challenging owing to the big volume of the data and the inherent ill-posedness.
Nonlinear Transform Induced Tensor Nuclear Norm for Tensor Completion
The main idea of this type of methods is exploiting the low-rank structure of frontal slices of the targeted tensor under the linear transform along the third mode.
Fully-Connected Tensor Network Decomposition for Robust Tensor Completion Problem
In this paper, by leveraging the superior expression of the fully-connected tensor network (FCTN) decomposition, we propose a $\textbf{FCTN}$-based $\textbf{r}$obust $\textbf{c}$onvex optimization model (RC-FCTN) for the RTC problem.
Nonlocal Patch-Based Fully-Connected Tensor Network Decomposition for Remote Sensing Image Inpainting
Different from other nonlocal patch-based methods, the NL-FCTN decomposition-based method, which increases tensor order by stacking similar small-sized patches to NSS groups, cleverly leverages the remarkable ability of FCTN decomposition to deal with higher-order tensors.
Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery
Recently, transform-based tensor nuclear norm minimization methods are considered to capture low-rank tensor structures to recover third-order tensors in multi-dimensional image processing applications.
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors
Under the unsupervised DIP framework, it is hypothesized and empirically demonstrated that proper neural network structures are reasonable priors of certain types of images, and the network weights can be learned without training data.
Dictionary Learning with Low-rank Coding Coefficients for Tensor Completion
In this paper, we propose a novel tensor learning and coding model for third-order data completion.
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior
Recently, convolutional neural network (CNN)-based methods are proposed for hyperspectral images (HSIs) denoising.
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling
This work employs an idea that models spectral images as tensors following the block-term decomposition model with multilinear rank-$(L_r, L_r, 1)$ terms (i. e., the LL1 model) and formulates the HSR problem as a coupled LL1 tensor decomposition problem.
Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergence
Key words: nonconvex optimization, tensor ring rank, logdet function, tensor completion, alternating direction method of multipliers.
Tensor train rank minimization with nonlocal self-similarity for tensor completion
The TT rank minimization accompany with \emph{ket augmentation}, which transforms a lower-order tensor (e. g., visual data) into a higher-order tensor, suffers from serious block-artifacts.
Spectrum Cartography via Coupled Block-Term Tensor Decomposition
Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i. e., a radio map)---from limited samples taken sparsely over the region.
Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion
In the literature, the tensor nuclear norm can be computed by using tensor singular value decomposition based on the discrete Fourier transform matrix, and tensor completion can be performed by the minimization of the tensor nuclear norm which is the relaxation of the sum of matrix ranks from all Fourier transformed matrix frontal slices.
Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections
In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images.
Deep Plug-and-play Prior for Low-rank Tensor Completion
By integrating deterministic low-rankness prior to the data-driven deep prior, we suggest a novel regularized tensor completion model for multi-dimensional image completion.
A Fast Algorithm for Cosine Transform Based Tensor Singular Value Decomposition
Recently, there has been a lot of research into tensor singular value decomposition (t-SVD) by using discrete Fourier transform (DFT) matrix.
Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery
Based on it, we define a novel tensor rank, the tensor $N$-tubal rank, as a vector whose elements contain the tubal rank of all mode-$k_1k_2$ unfolding tensors, to depict the correlations along different modes.
Rain Streak Removal for Single Image via Kernel Guided CNN
Then, our framework starts with learning the motion blur kernel, which is determined by two factors including angle and length, by a plain neural network, denoted as parameter net, from a patch of the texture component.
FastDeRain: A Novel Video Rain Streak Removal Method Using Directional Gradient Priors
In this paper, we propose a novel video rain streak removal approach FastDeRain, which fully considers the discriminative characteristics of rain streaks and the clean video in the gradient domain.
Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm
In this paper, we investigate tensor recovery problems within the tensor singular value decomposition (t-SVD) framework.
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part respectively.
A Novel Tensor-Based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors
Rain streaks removal is an important issue of the outdoor vision system and has been recently investigated extensively.
