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An optimal bilevel optimization model for the generalized total variation and anisotropic tensor parameters selection

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  • Boutaayamou, Idriss
  • Hadri, Aissam
  • Laghrib, Amine

Abstract

This paper investigates a novel variational optimization model for image denoising. Within this work, a bilevel optimization technique with a suitable mathematical background is proposed to detect automatically three crucial parameters: α0, α1 and θ. The parameters α0, α1 control the Total Generalized Variation (TGV) regularization while the parameter θ is related to the anisotropic diffusive tensor. A proper selection of these parameters represents a challenging task. Since these parameters are always related to a better approximation of the image gradient and texture, their computation plays a major role in preserving the image features. Analytically, we include results on the approximation of these parameters as well as the resolution of the encountered bilevel problem in a suitable framework. In addition, to resolve the PDE-constrained minimization problem, a modified primal-dual algorithm is proposed. Finally, numerical results are provided to remove noise and simultaneously keep safe fine details and important features with numerous comparisons to show the performance of the proposed approach.

Suggested Citation

  • Boutaayamou, Idriss & Hadri, Aissam & Laghrib, Amine, 2023. "An optimal bilevel optimization model for the generalized total variation and anisotropic tensor parameters selection," Applied Mathematics and Computation, Elsevier, vol. 438(C).
    • Handle: RePEc:eee:apmaco:v:438:y:2023:i:c:s0096300322005847
    DOI: 10.1016/j.amc.2022.127510
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    References listed on IDEAS

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    1. Chen, Dali & Chen, YangQuan & Xue, Dingyu, 2015. "Fractional-order total variation image denoising based on proximity algorithm," Applied Mathematics and Computation, Elsevier, vol. 257(C), pages 537-545.
    2. Alahyane, M. & Hakim, A. & Laghrib, A. & Raghay, S., 2019. "A lattice Boltzmann method applied to the fluid image registration," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 421-438.
    3. Min Tao, 2020. "Convergence study of indefinite proximal ADMM with a relaxation factor," Computational Optimization and Applications, Springer, vol. 77(1), pages 91-123, September.
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