重加权低秩张量补全及其在视频恢复中的应用
计算机视觉与模式识别
2017-07-11 v4
摘要
本文关注从随机破坏的不完整观测中恢复称为张量的多维数据。受用于稀疏性增强的重加权范数最小化的启发，本文提出一种重加权奇异值增强方案，以在张量补全过程中改善张量的低管秩。提出了一种基于t-SVD的高效迭代分解方案，显著改善低秩信号恢复。通过将所提方法应用于视频补全问题，确立了其有效性，实验结果显示该算法优于同类算法。
引用
@article{arxiv.1611.05964,
title = {Reweighted Low-Rank Tensor Completion and its Applications in Video Recovery},
author = {Baburaj M. and Sudhish N. George},
journal= {arXiv preprint arXiv:1611.05964},
year = {2017}
}
备注
Algorithm 1 is inefficient since line 2 is processed n 3 times need to be changed There are inconsistent notations throughout the manuscript Unitary Tensor are not defined