New Robust Tensor PCA via Affine Transformations and L2,1 Norms for Exact Tubal Low‐Rank Recovery from Highly Corrupted and Correlated Images in Signal …
P Liang, C Zhang, HT Likassa… - Mathematical Problems in …, 2022 - Wiley Online Library
In this latest work, the Newly Modified Robust Tensor Principal Component Analysis (New
RTPCA) using affine transformation and L2, 1 norms is proposed to remove the outliers and …
RTPCA) using affine transformation and L2, 1 norms is proposed to remove the outliers and …
Method for 1/f Fluctuation Component Extraction from Images and Its Application to Improve Kurume Kasuri Quality Estimation
J Shimazoe, K Arai, M Oda, J Oh - International Journal of …, 2022 - search.proquest.com
Method for 1/f fluctuation component extraction from images is proposed. As an application
of the proposed method, Kurume Kasuri textile quality evaluation is also proposed …
of the proposed method, Kurume Kasuri textile quality evaluation is also proposed …
GPR clutter suppression by online stochastic tensor decomposition
D Kumlu - Remote Sensing Letters, 2021 - Taylor & Francis
The clutter suppression is an active research area for ground-penetrating radar (GPR) and
the current trends use low rank and sparse decomposition (LRSD) based methods which …
the current trends use low rank and sparse decomposition (LRSD) based methods which …
Transform-based tensor singular value decomposition in multidimensional image recovery
Due to the limitation of imaging conditions, observed multidimensional images (eg, color
images, video, and multispectral/hyperspectral images) are unavoidably incomplete or …
images, video, and multispectral/hyperspectral images) are unavoidably incomplete or …
Global Weighted Tensor Nuclear Norm for Tensor Robust Principal Component Analysis
L Wang, Y Wang, S Wang, Y Liu, Y Hu, L Chen… - arXiv preprint arXiv …, 2022 - arxiv.org
Tensor Robust Principal Component Analysis (TRPCA), which aims to recover a low-rank
tensor corrupted by sparse noise, has attracted much attention in many real applications …
tensor corrupted by sparse noise, has attracted much attention in many real applications …
Multi-modal and frequency-weighted tensor nuclear norm for hyperspectral image denoising
X Xie, S Liu - arXiv preprint arXiv:2106.12489, 2021 - arxiv.org
Low-rankness is important in the hyperspectral image (HSI) denoising tasks. The tensor
nuclear norm (TNN), defined based on the tensor singular value decomposition, is a state-of …
nuclear norm (TNN), defined based on the tensor singular value decomposition, is a state-of …
[PDF][PDF] Recent Advances on Robust Tensor Principal Component Analysis with T-SVD
The task of robust tensor principal component analysis (RTPCA) is to separate the
underlying lowrank component and sparse component in highdimensional data. In RTPCA …
underlying lowrank component and sparse component in highdimensional data. In RTPCA …
[PDF][PDF] Recent Advances on Robust Tensor Principal Component Analysis
Recent Advances on Robust Tensor Principal Component Analysis Page 1 Recent Advances on
Robust Tensor Principal Component Analysis Lanlan Feng, Shenghan Wang, Ce Zhu, Yipeng …
Robust Tensor Principal Component Analysis Lanlan Feng, Shenghan Wang, Ce Zhu, Yipeng …