Search Results for author: Tai-Xiang Jiang

Found 16 papers, 2 papers with code

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

LAConv: Local Adaptive Convolution for Image Fusion

no code implementations24 Jul 2021 Zi-Rong Jin, Liang-Jian Deng, Tai-Xiang Jiang, Tian-Jing Zhang

The convolution operation is a powerful tool for feature extraction and plays a prominent role in the field of computer vision.

Hyperspectral Image Super-Resolution Image Super-Resolution +1

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.

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

Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation

no code implementations2 Sep 2020 Guangjing Song, Michael K. Ng, Tai-Xiang Jiang

In this paper, we develop a new alternating projection method to compute nonnegative low rank matrix approximation for nonnegative matrices.

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

Nonnegative Low Rank Tensor Approximation and its Application to Multi-dimensional Images

no code implementations28 Jul 2020 Tai-Xiang Jiang, Michael K. Ng, Junjun Pan, Guangjing Song

The main aim of this paper is to develop a new algorithm for computing nonnegative low rank tensor approximation for nonnegative tensors that arise in many multi-dimensional imaging applications.

Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks

no code implementations29 May 2020 Jin-Fan Hu, Ting-Zhu Huang, Liang-Jian Deng, Tai-Xiang Jiang, Gemine Vivone, Jocelyn Chanussot

In order to alleviate this issue, in this work, we propose a simple and efficient architecture for deep convolutional neural networks to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution hyperspectral image (HR-HSI).

Hyperspectral Image Super-Resolution Image Super-Resolution

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

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.

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