Dynamic MRI Reconstruction via Weighted Tensor Nuclear Norm Regularizer
IEEE Journal of Biomedical and Health Informatics, 2021In this paper, we propose a novel multi-dimensional reconstruction method based on the low-rank plus sparse tensor (L+S) decomposition model to reconstruct dynamic magnetic resonance imaging (dMRI). The multi-dimensional reconstruction method is formulated using a non-convex alternating direction method of multipliers (ADMM), where the weighted tensor ...
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Multiple graphs learning with a new weighted tensor nuclear norm
Neural Networks, 2021As an effective convex relaxation of the rank minimization model, the tensor nuclear norm minimization based multi-view clustering methods have been attracting more and more interest in recent years. However, most existing clustering methods regularize each singular value equally, restricting their capability and flexibility in tackling many practical ...
Xie, Deyan +4 more
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Fast Guaranteed Tensor Recovery with Adaptive Tensor Nuclear Norm
Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial IntelligenceReal-world datasets like multi-spectral images and videos are naturally represented as tensors. However, limitations in data acquisition often lead to corrupted or incomplete tensor data, making tensor recovery a critical challenge. Solving this problem requires exploiting inherent structural patterns, with the low-rank property being particularly ...
Jiangjun Peng +3 more
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A Corrected Tensor Nuclear Norm Minimization Method for Noisy Low-Rank Tensor Completion
SIAM Journal on Imaging Sciences, 2019Summary: In this paper, we study the problem of low-rank tensor recovery from limited sampling with noisy observations for third-order tensors. A tensor nuclear norm method based on a convex relaxation of the tubal rank of a tensor has been used and studied for tensor completion.
Xiongjun Zhang, Michael K. Ng
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Balanced Unfolding Induced Tensor Nuclear Norms for High-Order Tensor Completion
IEEE Transactions on Neural Networks and Learning SystemsThe recently proposed tensor tubal rank has been witnessed to obtain extraordinary success in real-world tensor data completion. However, existing works usually fix the transform orientation along the third mode and may fail to turn multidimensional low-tubal-rank structure into account.
Yuning Qiu +4 more
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Sparse and Truncated Nuclear Norm Based Tensor Completion
Neural Processing Letters, 2016One of the main difficulties in tensor completion is the calculation of the tensor rank. Recently a tensor nuclear norm, which is equal to the weighted sum of matrix nuclear norms of all unfoldings of the tensor, was proposed to address this issue. However, in this approach, all the singular values are minimized simultaneously.
Zi-Fa Han +3 more
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Coupled Transformed Induced Tensor Nuclear Norm for Robust Tensor Completion
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2023Mengjie Qin +5 more
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Logarithmic Norm Regularized Low-Rank Factorization for Matrix and Tensor Completion
IEEE Transactions on Image Processing, 2021Lin Chen, Xue Jiang, Xingzhao Liu
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Robust low tubal rank tensor completion via factor tensor norm minimization
Pattern Recognition, 2023cszhang0612 changsheng
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Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery
IEEE Transactions on Image Processing, 2022Yi-Si Luo, Xi-Le Zhao, Tai-Xiang Jiang
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