Reweighted Low-Rank Tensor Completion and its Applications in Video Recovery
This addresses video recovery from corrupted data, but appears incremental as it builds on existing reweighted norm and tensor completion techniques.
The paper tackles the problem of recovering multi-dimensional tensor data from incomplete observations by proposing a reweighted singular value enhancement scheme based on t-SVD, which significantly improves low-rank signal recovery in video completion applications and outperforms existing methods.
This paper focus on recovering multi-dimensional data called tensor from randomly corrupted incomplete observation. Inspired by reweighted $l_1$ norm minimization for sparsity enhancement, this paper proposes a reweighted singular value enhancement scheme to improve tensor low tubular rank in the tensor completion process. An efficient iterative decomposition scheme based on t-SVD is proposed which improves low-rank signal recovery significantly. The effectiveness of the proposed method is established by applying to video completion problem, and experimental results reveal that the algorithm outperforms its counterparts.