Tensor compressive sensing fused low-rankness and local-smoothness

Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

H Xu, C Fang, R Wang, S Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra
framework has shed new light on Tensor Robust Principal Component Analysis (TRPCA) …
被引用次数：1 相关文章 所有 2 个版本

Nonlocal Tensor Decomposition With Joint Low Rankness and Smoothness for Spectral CT Image Reconstruction

C Liu, S Li, D Hu, J Wang, W Qin, C Liu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Spectral computed tomography (CT) is a medical imaging technology that utilizes the
measurement of X-ray energy absorption in human tissue to obtain image information. It can …
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Compressive sensing of Internet traffic data using relative-error bound tensor-CUR decomposition

A Kumar, VV Saradhi, V Tamarapalli - Journal of Network and Computer …, 2024 - Elsevier
Missing values in the Internet traffic data pose a serious challenge for use in several traffic
engineering applications. Compressive sensing is a generic methodology for the …
被引用次数：1 相关文章 所有 2 个版本

Noisy tensor recovery via nonconvex optimization with theoretical recoverability

M Ding, J Yang, JJ Mei - Applied Mathematics Letters, 2024 - Elsevier
Noisy tensor recovery aims to estimate underlying low-rank tensors from the noisy
observations. Besides the sparse noise, tensor data can also be corrupted by the small …
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Tensor recovery from binary measurements fused low-rankness and smoothness

J Hou, X Liu, H Wang, K Guo - Signal Processing, 2024 - Elsevier
Tensor compressed sensing (TCS) from binary measurements aims to recover a tensor with
low-rankness from the binary quantization of its degraded linear measurements, which …
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Superposed Atomic Representation for Robust High-Dimensional Data Recovery of Multiple Low-Dimensional Structures

Y Wang - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
This paper proposes a unified Superposed Atomic Representation (SAR) framework for high-
dimensional data recovery with multiple low-dimensional structures. The data can be in …
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Modified correlated total variation regularization for low-rank matrix recovery

X Liu, Y Dou, J Wang - Signal, Image and Video Processing, 2024 - Springer
Image data often suffer from evident degradation like corruptions and missing values due to
the defects of image acquisition equipment. Low-Rank Matrix Recovery (LRMR) is an …
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