Next Article in Journal
Experimental Validation of a Novel Auto-Tuning Method for a Fractional Order PI Controller on an UR10 Robot
Previous Article in Journal
Layered Graphs: Applications and Algorithms
Open AccessArticle

Tensor Completion Based on Triple Tubal Nuclear Norm

1
School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian 223300, China
2
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
3
Jiangsu Province Key Construction Laboratory of Modern Measurement Technology and Intelligent System, Huaian 223300, China
4
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
5
Jiangsu Yaoshi Software Technology Co., Ltd., Nanjing 211103, China
6
Jiangsu Shuoshi Welding Technology Co., Ltd., Nanjing 211103, China
*
Author to whom correspondence should be addressed.
Algorithms 2018, 11(7), 94; https://doi.org/10.3390/a11070094
Received: 21 May 2018 / Revised: 16 June 2018 / Accepted: 19 June 2018 / Published: 28 June 2018
Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos. However, by interpreting in a circulant way, TNN only exploits tube (often carrying temporal/channel information) redundancy in a circulant way while preserving the row and column (often carrying spatial information) relationship. In this paper, a new tensor norm named the triple tubal nuclear norm (TriTNN) is proposed to simultaneously exploit tube, row and column redundancy in a circulant way by using a weighted sum of three TNNs. Thus, more spatial-temporal information can be mined. Further, a TriTNN-based tensor completion model with an ADMM solver is developed. Experiments on color images, videos and LiDAR datasets show the superiority of the proposed TriTNN against state-of-the-art nuclear norm-based tensor norms. View Full-Text
Keywords: tensor completion; tensor SVD; ADMM; image inpainting; video inpainting tensor completion; tensor SVD; ADMM; image inpainting; video inpainting
Show Figures

Figure 1

MDPI and ACS Style

Wei, D.; Wang, A.; Feng, X.; Wang, B.; Wang, B. Tensor Completion Based on Triple Tubal Nuclear Norm. Algorithms 2018, 11, 94.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Algorithms, EISSN 1999-4893, Published by MDPI AG
Back to TopTop