Multilevel Preconditioning Strategies for Convex Optimisation Methods in Image Deblurring
Please login to view abstract download link
Proximal gradient methods are widely used in imaging, and their 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 improvement in convergence speed compared to standard methods.
