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An Optimal Fluid Optical Flow Registration for Super-resolution with Lamé Parameters Learning

Author

Listed:
  • Abdelmajid El Hakoume

    (Sultan Moulay Slimane University)

  • Amine Laghrib

    (Sultan Moulay Slimane University)

  • Aissam Hadri

    (Ibn Zohr University)

  • Lekbir Afraites

    (Sultan Moulay Slimane University)

Abstract

The main idea of multi-frame super-resolution (SR) algorithms is to recover a single high-resolution image through a series of low-resolution ones of a captured scene. The success of the SR approaches is often related to well registration and restoration steps. In this work, we propose a new approach based on fluid optical flow image registration and a second-order regularization term to treat both the registration and restoration steps. The fluid registration is introduced to avoid misregistration errors, while the second-order regularization resolved by the Bregman iteration is employed to reduce the image artifacts. Moreover, we propose a bilevel supervised learning framework to compute the Lamé coefficients $$\lambda $$ λ and $$\mu $$ μ , which perform the nonparametric registration of the super-resolution result. The numerical part demonstrated that the proposed method copes with some competitive SR methods.

Suggested Citation

  • Abdelmajid El Hakoume & Amine Laghrib & Aissam Hadri & Lekbir Afraites, 2023. "An Optimal Fluid Optical Flow Registration for Super-resolution with Lamé Parameters Learning," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 508-538, May.
    • Handle: RePEc:spr:joptap:v:197:y:2023:i:2:d:10.1007_s10957-023-02186-4
    DOI: 10.1007/s10957-023-02186-4
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    References listed on IDEAS

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    1. Stephan Dempe & Felix Harder & Patrick Mehlitz & Gerd Wachsmuth, 2019. "Solving inverse optimal control problems via value functions to global optimality," Journal of Global Optimization, Springer, vol. 74(2), pages 297-325, June.
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