314 citations · 400 across the 8 of their papers we have counts for
4 papers · 1 filter
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm
Sheng Liu, Xiaozhen Xie, Wenfeng Kong
Hyperspectral images (HSIs) usually suffer from different types of pollution. This severely reduces the quality of HSIs and limits the accuracy of subsequent processing tasks. HSI…
Enhanced nonconvex low-rank approximation of tensor multi-modes for tensor completion
Haijin Zeng, Xiaozhen Xie, Jifeng Ning
Higher-order low-rank tensor arises in many data processing applications and has attracted great interests. Inspired by low-rank approximation theory, researchers have proposed a s…
Hyperspectral Image Restoration via Global Total Variation Regularized Local nonconvex Low-Rank matrix Approximation
Haijin Zeng, Xiaozhen Xie, Jifeng Ning
Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). Conventionally, the rank of LR…
Tensor completion using enhanced multiple modes low-rank prior and total variation
Haijin Zeng, Xiaozhen Xie, Jifeng Ning
In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode mat…