151 citations · 227 across the 7 of their papers we have counts for
7 papers
Multi-Dimensional Visual Data Recovery: Scale-Aware Tensor Modeling and Accelerated Randomized Computation
Wenjin Qin, Hailin Wang, Jiangjun Peng +2
The recently proposed fully-connected tensor network (FCTN) decomposition has demonstrated significant advantages in correlation characterization and transpositional invariance, an…
Accelerating Large-Scale Regularized High-Order Tensor Recovery
Wenjin Qin, Hailin Wang, Jingyao Hou +1
Currently, existing tensor recovery methods fail to recognize the impact of tensor scale variations on their structural characteristics. Furthermore, existing studies face prohibit…
Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation
Wenjin Qin, Hailin Wang, Feng Zhang +3
Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and…
Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
Hailin Wang, Jiangjun Peng, Wenjin Qin +2
The tensor data recovery task has thus attracted much research attention in recent years. Solving such an ill-posed problem generally requires to explore intrinsic prior structures…
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
Jiangjun Peng, Hailin Wang, Xiangyong Cao +3
Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based metho…
The block mutual coherence property condition for signal recovery
Jianwen Huang, Hailin Wang, Feng Zhang +2
Compressed sensing shows that a sparse signal can stably be recovered from incomplete linear measurements. But, in practical applications, some signals have additional structure, w…
An analysis of noise folding for low-rank matrix recovery
Jianwen Huang, Jianjun Wang, Feng Zhang +2
Previous work regarding low-rank matrix recovery has concentrated on the scenarios in which the matrix is noise-free and the measurements are corrupted by noise. However, in practi…