  Tensor completion is the problem of estimating the missing values of
high-order data from partially observed entries. Data corruption due to
prevailing outliers poses major challenges to traditional tensor completion
algorithms, which catalyzed the development of robust algorithms that alleviate
the effect of outliers. However, existing robust methods largely presume that
the corruption is sparse, which may not hold in practice. In this paper, we
develop a two-stage robust tensor completion approach to deal with tensor
completion of visual data with a large amount of gross corruption. A novel
coarse-to-fine framework is proposed which uses a global coarse completion
result to guide a local patch refinement process. To efficiently mitigate the
effect of a large number of outliers on tensor recovery, we develop a new
M-estimator-based robust tensor ring recovery method which can adaptively
identify the outliers and alleviate their negative effect in the optimization.
The experimental results demonstrate the superior performance of the proposed
approach over state-of-the-art robust algorithms for tensor completion.
