基于块和低秩张量恢复的视频去噪方法.

Since matrix representation of video data could damage its initial structure, this paper proposed a patch-based denoising method based on low-rank tensor recovery-Frist, it constructed a three order tensor through clustering similar patches in the preprocessing video sequences. Then according to low-rank property of video tensor and sparsity of noise artifacts，the proposed approach used the augmented Lagrange multipliers (ALM) to reconstruct the low-rank and sparse sensors, which could completely separate noise from the video tensor. This paper developed a tensor model to preserve the spatial structure of the video modality，thus it could remove the noise artifacts from complex video better. Simulation experiments show that this algorithm has the stronger ability of video denoising comparing with traditional methods. [ABSTRACT FROM AUTHOR]