367 citations · 406 across the 13 of their papers we have counts for
14 papers
Convergent regularization in inverse problems and linear plug-and-play denoisers
Andreas Hauptmann, Subhadip Mukherjee, Carola-Bibiane Schönlieb +1
Plug-and-play (PnP) denoising is a popular iterative framework for solving imaging inverse problems using off-the-shelf image denoisers. Their empirical success has motivated a lin…
Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging
William Herzberg, Andreas Hauptmann, Sarah J. Hamilton
Reconstruction of tomographic images from boundary measurements requires flexibility with respect to target domains. For instance, when the system equations are modeled by partial…
Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications
Leatile Marata, Onel Luis Alcaraz López, Andreas Hauptmann +2
Compressed sensing multi-user detection (CS-MUD) algorithms play a key role in optimizing grant-free (GF) non-orthogonal multiple access (NOMA) for massive machine-type communicati…
Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography
Andreas Hauptmann, Jenni Poimala
Learned iterative reconstructions hold great promise to accelerate tomographic imaging with empirical robustness to model perturbations. Nevertheless, an adoption for photoacoustic…
Sequential model correction for nonlinear inverse problems
Arttu Arjas, Mikko J. Sillanpää, Andreas Hauptmann
Inverse problems are in many cases solved with optimization techniques. When the underlying model is linear, first-order gradient methods are usually sufficient. With nonlinear mod…
Unsupervised denoising for sparse multi-spectral computed tomography
Satu I. Inkinen, Mikael A. K. Brix, Miika T. Nieminen +2
Multi-energy computed tomography (CT) with photon counting detectors (PCDs) enables spectral imaging as PCDs can assign the incoming photons to specific energy channels. However, P…
Robust Data-Driven Accelerated Mirror Descent
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +2
Learning-to-optimize is an emerging framework that leverages training data to speed up the solution of certain optimization problems. One such approach is based on the classical mi…
Sparsity promoting reconstructions via hierarchical prior models in diffuse optical tomography
Anssi Manninen, Meghdoot Mozumder, Tanja Tarvainen +1
Diffuse optical tomography (DOT) is a severely ill-posed nonlinear inverse problem that seeks to estimate optical parameters from boundary measurements. In the Bayesian framework,…
Learned reconstruction methods with convergence guarantees
Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem +2
In recent years, deep learning has achieved remarkable empirical success for image reconstruction. This has catalyzed an ongoing quest for precise characterization of correctness a…
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt +4
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep c…
Sequentially optimized projections in X-ray imaging
Martin Burger, Andreas Hauptmann, Tapio Helin +2
This work applies Bayesian experimental design to selecting optimal projection geometries in (discretized) parallel beam X-ray tomography assuming the prior and the additive noise…
Beltrami-Net: Domain Independent Deep D-bar Learning for Absolute Imaging with Electrical Impedance Tomography (a-EIT)
S. J. Hamilton, A. Hänninen, A. Hauptmann +1
Objective: To develop, and demonstrate the feasibility of, a novel image reconstruction method for absolute Electrical Impedance Tomography (a-EIT) that pairs deep learning techniq…
Networks for Nonlinear Diffusion Problems in Imaging
Simon Arridge, Andreas Hauptmann
A multitude of imaging and vision tasks have seen recently a major transformation by deep learning methods and in particular by the application of convolutional neural networks. Th…
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
Sarah Jane Hamilton, Andreas Hauptmann
The mathematical problem for Electrical Impedance Tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure…