A bilevel optimization problem with deep learning based on fractional total variation for image denoising

被引：3

作者：

Ben-loghfyry, Anouar ^{[1
]}

Hakim, Abdelilah ^{[2
]}

机构：

[1] Univ Hassan 2, Fac Sci & Technol Mohammedia, Dept Math, Casablanca, Morocco

[2] Univ Cadi Ayyad, LAMAI FST, Marrakech, Morocco

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2024年 / 83卷 / 10期

关键词：

Bilevel optimization; Neural network; Fractional total variation regularization; Imaging denoising; Inverse problems; INVERSE PROBLEMS; NEURAL-NETWORKS; RECONSTRUCTION; REGULARIZATION; DIFFUSION; FILTER;

D O I：

10.1007/s11042-023-16583-4

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this work, we introduce a bilevel problem based on fractional-order total variation for image denoising. A deep learning architecture is provided in the upper model to strengthen the regularization term in the lower one. Numerical results show the efficacy of the proposed approach, compared to various competitive models.

引用

页码：28595 / 28614

页数：20

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