Search Results for author: Xi-Le Zhao

Found 24 papers, 3 papers with code

Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing

no code implementations8 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.

Hyperspectral Unmixing Tensor Decomposition

HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging

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.

Compressive Sensing Denoising

Fully-Connected Tensor Network Decomposition for Robust Tensor Completion Problem

no code implementations17 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.

Video Background Subtraction

Nonlinear Transform Induced Tensor Nuclear Norm for Tensor Completion

no code implementations17 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.

Nonlocal Patch-Based Fully-Connected Tensor Network Decomposition for Remote Sensing Image Inpainting

no code implementations13 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.

Image Inpainting

Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery

no code implementations29 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.

Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors

no code implementations24 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.

Image Denoising

Dictionary Learning with Low-rank Coding Coefficients for Tensor Completion

no code implementations26 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.

Dictionary Learning

Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior

no code implementations22 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.

Denoising

Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling

no code implementations18 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.

Super-Resolution Tensor Decomposition

Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergence

no code implementations14 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.

Tensor train rank minimization with nonlocal self-similarity for tensor completion

no code implementations29 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.

Spectrum Cartography via Coupled Block-Term Tensor Decomposition

no code implementations28 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.

Spectrum Cartography Tensor Decomposition

Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion

no code implementations16 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.

Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections

no code implementations15 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.

Hyperspectral Image Denoising Image Denoising

Deep Plug-and-play Prior for Low-rank Tensor Completion

no code implementations11 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.

Denoising

A Fast Algorithm for Cosine Transform Based Tensor Singular Value Decomposition

no code implementations8 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.

Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery

no code implementations3 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.

Rain Streak Removal for Single Image via Kernel Guided CNN

no code implementations26 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.

FastDeRain: A Novel Video Rain Streak Removal Method Using Directional Gradient Priors

3 code implementations20 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.

Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm

4 code implementations15 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.

Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition

no code implementations8 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.

Image Restoration Tensor Decomposition

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