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	Low-Rank High-Order Tensor Completion With Applications in Visual Data
	Qin, Wenjin1; Wang, Hailin2; Zhang, Feng1; Wang, Jianjun1,3; Luo, Xin4,5; Huang, Tingwen6
	2022
摘要	Recently, tensor Singular Value Decomposition (t-SVD)-based low-rank tensor completion (LRTC) has achieved unprecedented success in addressing various pattern analysis issues. However, existing studies mostly focus on third-order tensors while order-d (d >= 4) tensors are commonly encountered in real-world applications, like fourth-order color videos, fourth-order hyper-spectral videos, fifth-order light-field images, and sixth-order bidirectional texture functions. Aiming at addressing this critical issue, this paper establishes an order-d tensor recovery framework including the model, algorithm and theories by innovatively developing a novel algebraic foundation for order-d t-SVD, thereby achieving exact completion for any order-d low t-SVD rank tensors with missing values with an overwhelming probability. Emperical studies on synthetic data and real-world visual data illustrate that compared with other state-of-the-art recovery frameworks, the proposed one achieves highly competitive performance in terms of both qualitative and quantitative metrics. In particular, as the observed data density becomes low, i.e., about 10%, the proposed recovery framework is still significantly better than its peers. The code of our algorithm is released at https://github.com/Qinwenjinswu/TIP-Code
关键词	Low-rank order-d tensor completion order-d tensor singular value decomposition invertible linear transforms convex optimization sparsity measure
DOI	10.1109/TIP.2022.3155949
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	31页码:2433-2448
通讯作者	Wang, Jianjun(wjj@swu.edu.cn)
收录类别	SCI
WOS记录号	WOS:000769973200009
语种	英语