Low-Rank High-Order Tensor Completion With Applications in Visual Data. Academic Article uri icon


  • Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion (LRTC) has achieved unprecedented success in addressing various pattern analysis issues. However, existing studies mostly focus on third-order tensors while order- d ( d 4 ) tensors are commonly encountered in real-world applications, like fourth-order color videos, fourth-order hyper-spectral videos, fifth-order light-field images, and sixth-order bidirectional texture functions. Aiming at addressing this critical issue, this paper establishes an order- d tensor recovery framework including the model, algorithm and theories by innovatively developing a novel algebraic foundation for order- d t-SVD, thereby achieving exact completion for any order- d low t-SVD rank tensors with missing values with an overwhelming probability. Emperical studies on synthetic data and real-world visual data illustrate that compared with other state-of-the-art recovery frameworks, the proposed one achieves highly competitive performance in terms of both qualitative and quantitative metrics. In particular, as the observed data density becomes low, i.e., about 10%, the proposed recovery framework is still significantly better than its peers. The code of our algorithm is released at https://github.com/Qinwenjinswu/TIP-Code.

published proceedings

  • IEEE Trans Image Process

altmetric score

  • 0.25

author list (cited authors)

  • Qin, W., Wang, H., Zhang, F., Wang, J., Luo, X., & Huang, T.

citation count

  • 16

complete list of authors

  • Qin, Wenjin||Wang, Hailin||Zhang, Feng||Wang, Jianjun||Luo, Xin||Huang, Tingwen

publication date

  • January 2022