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

Abdelmajid El Hakoume, Amine Laghrib (), Aissam Hadri and Lekbir Afraites
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Abdelmajid El Hakoume: Sultan Moulay Slimane University
Amine Laghrib: Sultan Moulay Slimane University
Aissam Hadri: Ibn Zohr University
Lekbir Afraites: Sultan Moulay Slimane University

Journal of Optimization Theory and Applications, 2023, vol. 197, issue 2, No 5, 508-538

Abstract: 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.

Keywords: Super-resolution; Optical flow; Fluid registration; Bilevel; Regularization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10957-023-02186-4

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