  The t-SVD based Tensor Robust Principal Component Analysis (TRPCA) decomposes
low rank multi-linear signal corrupted by gross errors into low multi-rank and
sparse component by simultaneously minimizing tensor nuclear norm and l 1 norm.
But if the multi-rank of the signal is considerably large and/or large amount
of noise is present, the performance of TRPCA deteriorates. To overcome this
problem, this paper proposes a new efficient iterative reweighted tensor
decomposition scheme based on t-SVD which significantly improves tensor
multi-rank in TRPCA. Further, the sparse component of the tensor is also
recovered by reweighted l 1 norm which enhances the accuracy of decomposition.
The effectiveness of the proposed method is established by applying it to the
video denoising problem and the experimental results reveal that the proposed
algorithm outperforms its counterparts.
