Image Completion in Embedded Space Using Multistage Tensor Ring Decomposition

Farnaz Sedighin, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review


Tensor Completion is an important problem in big data processing. Usually, data acquired from different aspects of a multimodal phenomenon or different sensors are incomplete due to different reasons such as noise, low sampling rate or human mistake. In this situation, recovering the missing or uncertain elements of the incomplete dataset is an important step for efficient data processing. In this paper, a new completion approach using Tensor Ring (TR) decomposition in the embedded space has been proposed. In the proposed approach, the incomplete data tensor is first transformed into a higher order tensor using the block Hankelization method. Then the higher order tensor is completed using TR decomposition with rank incremental and multistage strategy. Simulation results show the effectiveness of the proposed approach compared to the state of the art completion algorithms, especially for very high missing ratios and noisy cases.

Original languageEnglish
Article number687176
JournalFrontiers in Artificial Intelligence
Publication statusPublished - 13 Aug 2021


  • Hankelization
  • image completion
  • multistage strategy
  • tensor ring decomposition
  • tensorization


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