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Journal Articles Journal of Optimization Theory and Applications Year : 2022

A Tensor Regularized Nuclear Norm Method for Image and Video Completion

A. Bentbib
  • Function : Author
A. El Hachimi
  • Function : Author
A. Ratnani

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.
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Dates and versions

hal-04413484 , version 1 (23-01-2024)

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A. Bentbib, A. El Hachimi, Khalide Jbilou, A. Ratnani. A Tensor Regularized Nuclear Norm Method for Image and Video Completion. Journal of Optimization Theory and Applications, 2022, 192 (2), pp.401-425. ⟨10.1007/s10957-021-01947-3⟩. ⟨hal-04413484⟩
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