A Tensor Regularized Nuclear Norm Method for Image and Video Completion

被引：0
作者：
A. H. Bentbib
A. El Hachimi
K. Jbilou
A. Ratnani
机构：
[1] Laboratoire de Mathématiques Appliquées,Faculté des Sciences et Techniques
[2] Mohammed VI Polytechnic University,Gueliz
[3] Université du Littoral Cote d’Opale,Laboratory MSDA
来源：
Journal of Optimization Theory and Applications | 2022年 / 192卷
关键词：
ADMM; Tensor completion; Tensor nuclear norm; T-product; T-SVD;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In the present paper, we propose two new methods for tensor completion of third-order tensors. The proposed methods consist in minimizing the average rank of the underlying tensor using its approximate function, namely the tensor nuclear norm. The recovered data will be obtained by combining the minimization process with the total variation regularization technique. We will adopt the alternating direction method of multipliers, using the tensor T-product, to solve the main optimization problems associated with the two proposed algorithms. In the last section, we present some numerical experiments and comparisons with the most known image video completion methods.
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页码：401 / 425
页数：24
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