| Autor: |
Nachaoui, Mourad, Afraites, Lekbir, Hadri, Aissam, Laghrib, Amine |
| Předmět: |
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| Zdroj: |
Communications on Pure & Applied Analysis; Apr2022, Vol. 21 Issue 4, p1249-1291, 43p |
| Abstrakt: |
This paper introduce a novel optimization procedure to reduce mixture of Gaussian and impulse noise from images. This technique exploits a non-convex PDE-constrained characterized by a fractional-order operator. The used non-convex term facilitated the impulse component approximation controlled by a spatial parameter γ. A non-convex and non-smooth bi-level optimization framework with a modified projected gradient algorithm is then proposed in order to learn the parameter γ. Denoising tests confirm that the non-convex term and learned parameter γ lead in general to an improved reconstruction when compared to results of convex norm and manual parameter λ choice. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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