1 citations · 1 across the 3 of their papers we have counts for
4 papers
Trust your source: quantifying source condition elements for variational regularisation methods
Martin Benning, Tatiana A. Bubba, Luca Ratti +1
Source conditions are a key tool in regularisation theory that are needed to derive error estimates and convergence rates for ill-posed inverse problems. In this paper, we provide…
Bilevel learning of regularization models and their discretization for image deblurring and super-resolution
Tatiana A. Bubba, Luca Calatroni, Ambra Catozzi +6
Bilevel learning is a powerful optimization technique that has extensively been employed in recent years to bridge the world of model-driven variational approaches with data-driven…
Convergent Data-driven Regularizations for CT Reconstruction
Samira Kabri, Alexander Auras, Danilo Riccio +4
The reconstruction of images from their corresponding noisy Radon transform is a typical example of an ill-posed linear inverse problem as arising in the application of computerize…
Regularization of Inverse Problems: Deep Equilibrium Models versus Bilevel Learning
Danilo Riccio, Matthias J. Ehrhardt, Martin Benning
Variational regularization methods are commonly used to approximate solutions of inverse problems. In recent years, model-based variational regularization methods have often been r…