no code implementations • 7 Jul 2024 • Ting-Wei Zhou, Xi-Le Zhao, Jian-Li Wang, Yi-Si Luo, Min Wang, Xiao-Xuan Bai, Hong Yan
Especially, the deep latent generative module can faithfully generate the latent tensor as compared with shallow matrix factorization.
no code implementations • 22 Jan 2024 • Zhiyu Liu, Zhi Han, Yandong Tang, Xi-Le Zhao, Yao Wang
This paper considers the problem of recovering a tensor with an underlying low-tubal-rank structure from a small number of corrupted linear measurements.
no code implementations • CVPR 2024 • Yu-Bang Zheng, Xi-Le Zhao, Junhua Zeng, Chao Li, Qibin Zhao, Heng-Chao Li, Ting-Zhu Huang
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.
no code implementations • 8 May 2022 • Meng Ding, Xiao Fu, Xi-Le Zhao
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.
1 code implementation • 20 Mar 2022 • Xinyu Chen, ChengYuan Zhang, Xi-Le Zhao, Nicolas Saunier, Lijun Sun
Modern time series datasets are often high-dimensional, incomplete/sparse, and nonstationary.
no code implementations • CVPR 2022 • YiSi Luo, Xi-Le Zhao, Deyu Meng, Tai-Xiang Jiang
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.
no code implementations • 17 Oct 2021 • Ben-Zheng Li, Xi-Le Zhao, Teng-Yu Ji, Xiong-Jun Zhang, Ting-Zhu Huang
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.
no code implementations • 17 Oct 2021 • Yun-Yang Liu, Xi-Le Zhao, Guang-Jing Song, Yu-Bang Zheng, Ting-Zhu Huang
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.
no code implementations • 13 Sep 2021 • Wen-Jie Zheng, Xi-Le Zhao, Yu-Bang Zheng, Zhi-Feng Pang
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.
no code implementations • 29 May 2021 • Yi-Si Luo, Xi-Le Zhao, Tai-Xiang Jiang, Yi Chang, Michael K. Ng, Chao Li
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.
no code implementations • 24 Feb 2021 • Yu-Chun Miao, Xi-Le Zhao, Xiao Fu, Jian-Li Wang, Yu-Bang Zheng
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.
no code implementations • 26 Sep 2020 • Tai-Xiang Jiang, Xi-Le Zhao, Hao Zhang, Michael K. Ng
In this paper, we propose a novel tensor learning and coding model for third-order data completion.
no code implementations • 22 Aug 2020 • Yi-Si Luo, Xi-Le Zhao, Tai-Xiang Jiang, Yu-Bang Zheng, Yi Chang
Recently, convolutional neural network (CNN)-based methods are proposed for hyperspectral images (HSIs) denoising.
no code implementations • 18 Jun 2020 • Meng Ding, Xiao Fu, Ting-Zhu Huang, Jun Wang, Xi-Le Zhao
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.
no code implementations • 14 May 2020 • Meng Ding, Ting-Zhu Huang, Xi-Le Zhao, Tian-Hui Ma
Key words: nonconvex optimization, tensor ring rank, logdet function, tensor completion, alternating direction method of multipliers.
no code implementations • 29 Apr 2020 • Meng Ding, Ting-Zhu Huang, Xi-Le Zhao, Michael K. Ng, Tian-Hui Ma
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.
no code implementations • 28 Nov 2019 • Guoyong Zhang, Xiao Fu, Jun Wang, Xi-Le Zhao, Mingyi Hong
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.
no code implementations • 16 Sep 2019 • Tai-Xiang Jiang, Michael K. Ng, Xi-Le Zhao, Ting-Zhu Huang
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.
no code implementations • 15 May 2019 • Hao Zhang, Xi-Le Zhao, Tai-Xiang Jiang, Michael Kwok-Po Ng
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.
no code implementations • 11 May 2019 • Xi-Le Zhao, Wen-Hao Xu, Tai-Xiang Jiang, Yao Wang, Michael Ng
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.
no code implementations • 8 Feb 2019 • Wen-Hao Xu, Xi-Le Zhao, Michael Ng
Recently, there has been a lot of research into tensor singular value decomposition (t-SVD) by using discrete Fourier transform (DFT) matrix.
no code implementations • 3 Dec 2018 • Yu-Bang Zheng, Ting-Zhu Huang, Xi-Le Zhao, Tai-Xiang Jiang, Teng-Yu Ji, Tian-Hui Ma
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.
no code implementations • 26 Aug 2018 • Ye-Tao Wang, Xi-Le Zhao, Tai-Xiang Jiang, Liang-Jian Deng, Yi Chang, Ting-Zhu Huang
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.
3 code implementations • 20 Mar 2018 • Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng, Yao Wang
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.
4 code implementations • 15 Dec 2017 • Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng
In this paper, we investigate tensor recovery problems within the tensor singular value decomposition (t-SVD) framework.
no code implementations • 8 Jul 2017 • Yao Wang, Jiangjun Peng, Qian Zhao, Deyu Meng, Yee Leung, Xi-Le Zhao
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.
no code implementations • CVPR 2017 • Tai-Xiang Jiang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng, Yao Wang
Rain streaks removal is an important issue of the outdoor vision system and has been recently investigated extensively.