21 citations · 21 across the 2 of their papers we have counts for
7 papers
Learned convex regularizers for inverse problems
Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov +3
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.…
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset
Sören Dittmer, Tobias Kluth, Mads Thorstein Roar Henriksen +1
Magnetic particle imaging (MPI) is an imaging modality exploiting the nonlinear magnetization behavior of (super-)paramagnetic nanoparticles to obtain a space- and often also time-…
Ground Truth Free Denoising by Optimal Transport
Sören Dittmer, Carola-Bibiane Schönlieb, Peter Maass
We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals. The training is solely based on samples of n…
A Projectional Ansatz to Reconstruction
Sören Dittmer, Peter Maass
Recently the field of inverse problems has seen a growing usage of mathematically only partially understood learned and non-learned priors. Based on first principles, we develop a…
Regularization by architecture: A deep prior approach for inverse problems
Sören Dittmer, Tobias Kluth, Peter Maass +1
The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems. DIP networks have been recently introduced for applications in i…
Singular Values for ReLU Layers
Sören Dittmer, Emily J. King, Peter Maass
Despite their prevalence in neural networks we still lack a thorough theoretical characterization of ReLU layers. This paper aims to further our understanding of ReLU layers by stu…