A Tensor Regularized Nuclear Norm Method for Image and Video Completion

Autor:	A. El Hachimi, A. H. Bentbib, Khalide Jbilou, Ahmed Ratnani
Rok vydání:	2021
Předmět:	Control and Optimization Optimization problem Rank (linear algebra) Applied Mathematics Matrix norm Function (mathematics) Minification Tensor Management Science and Operations Research Total variation denoising Algorithm Mathematics Image (mathematics)
Zdroj:	Journal of Optimization Theory and Applications. 192:401-425
ISSN:	1573-2878 0022-3239
DOI:	10.1007/s10957-021-01947-3
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0b4c89aa57b5625ad16e3ae527892e49 https://doi.org/10.1007/s10957-021-01947-3 Zobrazit plný text záznamu Full text from SpringerLink