计算机应用研究2017，Vol.34Issue(4)：1273-1276,1280,5.DOI:10.3969/j.issn.1001-3695.2017.04.072
基于块和低秩张量恢复的视频去噪方法
Patch-based video denoising using low-rank tensor recovery
摘要
Abstract
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.关键词
视频去噪/张量恢复/鲁棒主成分分析/增广拉格朗日乘子法Key words
video denoising/tensor recovery/robust principal component analysis/augmented Lagrange multipliers分类
信息技术与安全科学引用本文复制引用
李小利,杨晓梅,陈代斌..基于块和低秩张量恢复的视频去噪方法[J].计算机应用研究,2017,34(4):1273-1276,1280,5.
