Learned Regularization for Inverse Problems: Insights from a Spectral Model

no code implementations • 15 Dec 2023 • Martin Burger, Samira Kabri

The aim of this paper is to provide a theoretically founded investigation of state-of-the-art learning approaches for inverse problems.

Resolution-Invariant Image Classification based on Fourier Neural Operators

1 code implementation • 2 Apr 2023 • Samira Kabri, Tim Roith, Daniel Tenbrinck, Martin Burger

In this paper we investigate the use of Fourier Neural Operators (FNOs) for image classification in comparison to standard Convolutional Neural Networks (CNNs).

Classification Image Classification

Convergent Data-driven Regularizations for CT Reconstruction

1 code implementation • 14 Dec 2022 • Samira Kabri, Alexander Auras, Danilo Riccio, Hartmut Bauermeister, Martin Benning, Michael Moeller, Martin Burger

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 computerized tomography (CT).