  Tensor completion is an important problem in modern data analysis. In this
work, we investigate a specific sampling strategy, referred to as tubal
sampling. We propose two novel non-convex tensor completion frameworks that are
easy to implement, named tensor $L_1$-$L_2$ (TL12) and tensor completion via
CUR (TCCUR). We test the efficiency of both methods on synthetic data and a
color image inpainting problem. Empirical results reveal a trade-off between
the accuracy and time efficiency of these two methods in a low sampling ratio.
Each of them outperforms some classical completion methods in at least one
aspect.
