With the advance of acquisition techniques, plentiful higherorder tensor data sets are built up in a great variety of fields such as computer vision, neuroscience, remote sensing and recommender systems. The real-world tensors often contain missing values, which makes tensor completion become a prerequisite to utilize them. Previous studies have shown that imposing a low-rank constraint on tensor completion produces impressive performances. In this paper, we argue that low-rank constraint, albeit useful, is not effective enough to exploit the local smooth and piecewise priors of visual data. We propose integrating total variation into low-rank tensor completion (LRTC) to address the drawback. As LRTC can be formulated by both tensor unfoldi...
The goal of tensor completion is to recover a tensor from a subset of its entries, often by exploiti...
Multi-dimensional images, such as color images and multi-spectral images (MSIs), are highly correlat...
Multi-dimensional images, such as color images and multi-spectral images (MSIs), are highly correlat...
We propose a new tensor completion method based on tensor trains. The to-be-completed tensor is mode...
We propose a new tensor completion method based on tensor trains. The to-be-completed tensor is mode...
This paper proposes a novel formulation of the tensor completion problem to impute missing entries o...
© 1992-2012 IEEE. This paper proposes a novel approach to tensor completion, which recovers missing ...
Many restoration methods use the low-rank constraint of high-dimensional image signals to recover co...
Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of...
Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of...
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) st...
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) st...
Low-rank tensor completion is a recent method for estimating the values of the missing elements in t...
Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-valued data...
Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-valued data...
The goal of tensor completion is to recover a tensor from a subset of its entries, often by exploiti...
Multi-dimensional images, such as color images and multi-spectral images (MSIs), are highly correlat...
Multi-dimensional images, such as color images and multi-spectral images (MSIs), are highly correlat...
We propose a new tensor completion method based on tensor trains. The to-be-completed tensor is mode...
We propose a new tensor completion method based on tensor trains. The to-be-completed tensor is mode...
This paper proposes a novel formulation of the tensor completion problem to impute missing entries o...
© 1992-2012 IEEE. This paper proposes a novel approach to tensor completion, which recovers missing ...
Many restoration methods use the low-rank constraint of high-dimensional image signals to recover co...
Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of...
Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of...
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) st...
Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) st...
Low-rank tensor completion is a recent method for estimating the values of the missing elements in t...
Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-valued data...
Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-valued data...
The goal of tensor completion is to recover a tensor from a subset of its entries, often by exploiti...
Multi-dimensional images, such as color images and multi-spectral images (MSIs), are highly correlat...
Multi-dimensional images, such as color images and multi-spectral images (MSIs), are highly correlat...