Reweighted Low-Rank Tensor Decomposition based on t-SVD and its Applications in Video Denoising
This work addresses a specific bottleneck in tensor decomposition for video denoising, offering incremental improvements over prior methods.
The paper tackles the problem of performance deterioration in Tensor Robust Principal Component Analysis (TRPCA) when dealing with high multi-rank signals or large noise, by proposing an iterative reweighted tensor decomposition scheme based on t-SVD that improves tensor multi-rank and enhances sparse component recovery. The result shows that the proposed algorithm outperforms existing methods in video denoising applications.
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.