  Many tensor-based data completion methods aim to solve image and video
in-painting problems. But, all methods were only developed for a single
dataset. In most of real applications, we can usually obtain more than one
dataset to reflect one phenomenon, and all the datasets are mutually related in
some sense. Thus one question raised whether such the relationship can improve
the performance of data completion or not? In the paper, we proposed a novel
and efficient method by exploiting the relationship among datasets for
multi-video data completion. Numerical results show that the proposed method
significantly improve the performance of video in-painting, particularly in the
case of very high missing percentage.
