Low-Rank Tensor Completion Using Matrix Factorization Based on Tensor Train Rank and Total Variation

@article{Ding2019LowRankTC,
  title={Low-Rank Tensor Completion Using Matrix Factorization Based on Tensor Train Rank and Total Variation},
  author={Meng Ding and Tingzhu Huang and Teng-Yu Ji and Xile Zhao and Jinghua Yang},
  journal={Journal of Scientific Computing},
  year={2019},
  volume={81},
  pages={941 - 964},
  url={https://api.semanticscholar.org/CorpusID:202938666}
}
An optimization model combining low-rank matrix factorization based on tensor train (TT) rank and the total variation to retain the strength of TT rank and alleviate block-artifacts is built.
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mengding56.github.io
80 Citations
Background Citations
22
Methods Citations
17

80 Citations

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