A Tensor Regularized Nuclear Norm Method for Image and Video Completion

A. H. Bentbib (), A. El Hachimi, K. Jbilou () and A. Ratnani ()
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A. H. Bentbib: Laboratoire de Mathématiques Appliquées
A. El Hachimi: Mohammed VI Polytechnic University
K. Jbilou: Mohammed VI Polytechnic University
A. Ratnani: Mohammed VI Polytechnic University

Journal of Optimization Theory and Applications, 2022, vol. 192, issue 2, No 1, 425 pages

Abstract: Abstract 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.

Keywords: ADMM; Tensor completion; Tensor nuclear norm; T-product; T-SVD (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-021-01947-3

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