Multilevel Preconditioning Strategies for Convex Optimization Methods in Image Deblurring
Claudia Binda (post-doc Univeristà dell'Insubria, Como, Italie)
| When |
Mar 25, 2026
from 01:00 to 02:00 |
|---|---|
| Where | M7 101 |
| Attendees |
Claudia Binda |
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CLAUDIA BINDA
Title: Multilevel Preconditioning Strategies for Convex Optimization Methods in Image Deblurring
Abstract:
Proximal gradient methods are widely used in imaging, and their speed of convergence can be accelerated by incorporating variable metrics and/or extrapolation steps. Recent works have shown that preconditioning strategies can significantly enhance this acceleration, in particular, for image deblurring problems. In parallel, a multilevel framework has been introduced to speed up inertial and inexact forward–backward schemes for image restoration problems.
In this talk, we combine preconditioning and multilevel strategies to design a robust and consistent acceleration framework for both standard and inexact forward–backward schemes applied to regularized convex optimization problems. Numerical experiments in image deblurring confirm that our approach yields a substantial improvementin convergence speed compared to standard methods.
Website:
In Room M7 101, 1st floor, Monod campus, ENSL (to be confirmed).