Low-Rank Tensor Completion with Total Variation for Visual Data Inpainting


  • Xutao Li Harbin Institute of Technology
  • Yunming Ye Harbin Institute of Technology
  • Xiaofei Xu Harbin Institute of Technology




tensor completion, total variation, inpainting, low rank, ADMM


With the advance of acquisition techniques, plentiful higherorder tensor data sets are built up in a great variety of fields such as computer vision, neuroscience, remote sensing and recommender systems. The real-world tensors often contain missing values, which makes tensor completion become a prerequisite to utilize them. Previous studies have shown that imposing a low-rank constraint on tensor completion produces impressive performances. In this paper, we argue that low-rank constraint, albeit useful, is not effective enough to exploit the local smooth and piecewise priors of visual data. We propose integrating total variation into low-rank tensor completion (LRTC) to address the drawback. As LRTC can be formulated by both tensor unfolding and tensor decomposition, we develop correspondingly two methods, namely LRTC-TV-I and LRTC-TVII, and their iterative solvers. Extensive experimental results on color image and medical image inpainting tasks show the effectiveness and superiority of the two methods against state-of-the-art competitors.




How to Cite

Li, X., Ye, Y., & Xu, X. (2017). Low-Rank Tensor Completion with Total Variation for Visual Data Inpainting. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10776