3 citations · 6 across the 4 of their papers we have counts for
10 papers
LAConv: Local Adaptive Convolution for Image Fusion
Zi-Rong Jin, Liang-Jian Deng, Tai-Xiang Jiang +1
The convolution operation is a powerful tool for feature extraction and plays a prominent role in the field of computer vision. However, when targeting the pixel-wise tasks like im…
Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix Approximation
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. In the nonnegative low rank matrix appr…
Dictionary Learning with Low-rank Coding Coefficients for Tensor Completion
Tai-Xiang Jiang, Xi-Le Zhao, Hao Zhang +1
In this paper, we propose a novel tensor learning and coding model for third-order data completion. Our model is to learn a data-adaptive dictionary from the given observations, an…
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks
Jin-Fan Hu, Ting-Zhu Huang, Liang-Jian Deng +3
Hyperspectral images are of crucial importance in order to better understand features of different materials. To reach this goal, they leverage on a high number of spectral bands.…
Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion
Tai-Xiang Jiang, Michael K. Ng, Xi-Le Zhao +1
The main aim of this paper is to develop a framelet representation of the tensor nuclear norm for third-order tensor completion. In the literature, the tensor nuclear norm can be c…
Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections
Hao Zhang, Xi-Le Zhao, Tai-Xiang Jiang +1
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…