Reweighted Low-Rank Tensor Decomposition based on t-SVD and its Applications in Video Denoising

TL;DR

This paper introduces a reweighted tensor decomposition method based on t-SVD that enhances low-rank tensor recovery, especially in noisy conditions, and demonstrates superior performance in video denoising tasks.

Contribution

It proposes an iterative reweighted tensor decomposition scheme based on t-SVD, improving tensor rank approximation and sparse component recovery in TRPCA.

Findings

Outperforms existing methods in video denoising accuracy

Effectively handles large multi-rank tensors with noise

Enhances tensor decomposition precision

Abstract

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.