% This file was created by ZeTeM Website
 
% Alexander Denker







@ARTICLE{Altenkrueger2023_28b26,
  author = {Fabian Altenkrüger and Alexander Denker and Paul Hagemann and Peter Maaß and Gabriele Steidl},
  title = { PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization},
  journal = {Inverse Problems},
  volume = {39},
  number = {6},
  year = {2023}
}


@ARTICLE{Arndt2023_d6b14,
  author = {Clemens Arndt and Alexander Denker and Sören Dittmer and Nick Heilenkötter and Meira Iske and Tobias Kluth and Peter Maaß and Judith Nickel},
  title = {Invertible residual networks in the context of regularization theory for linear inverse problems},
  journal = {Inverse Problems},
  volume = {39},
  number = {12},
  publisher = {IOPscience},
  year = {2023},
  doi = {10.1088/1361-6420/ad0660}
}


@ARTICLE{Arndt2023_c4b93,
  author = {Clemens Arndt and Alexander Denker and Sören Dittmer and Johannes Leuschner and Judith Nickel and Maximilian Schmidt},
  title = {Model-based deep learning approaches to the Helsinki Tomography Challenge 2022},
  journal = {Applied Mathematics for Modern Challenges},
  volume = {1},
  number = {2},
  year = {2023},
  doi = {10.3934/ammc.2023007}
}

 
@UNPUBLISHED{Denker2023_c19ca,
  author = {Alexander Denker and Imraj Singh and R. Barbano and Z. Kereta and Bangti Jin and Kris Thielemans and Peter Maaß and Simon Arridge},
  title = {Score-Based Generative Models for PET Image Reconstruction},
  journal = {Machine Learning for Biomedical Imaging}
}


@ARTICLE{Barbano2022_4dd3b,
  author = {R. Barbano and Johannes Leuschner and Maximilian Schmidt and Alexander Denker and Andreas Hauptmann and Peter Maaß and Bangti Jin},
  title = {An Educated Warm Start For Deep Image Prior-based Micro CT Reconstruction},
  journal = {IEEE Transactions on Computational Imaging},
  volume = {8},
  pages = {1210-1222},
  year = {2022},
  doi = {10.1109/TCI.2022.3233188}
}


@ARTICLE{Arndt2022_916b9,
  author = {Clemens Arndt and Alexander Denker and Judith Nickel and Johannes Leuschner and Maximilian Schmidt and Gael Rigaud},
  title = {In Focus - hybrid deep learning approaches to the HDC2021 challenge},
  journal = {Inverse Problems and Imaging},
  year = {2022},
  doi = {10.3934/ipi.2022061}
}


@ARTICLE{Denker2021_ef45b,
  author = {Alexander Denker and Maximilian Schmidt and Johannes Leuschner and Peter Maaß},
  title = {Conditional Invertible Neural Networks for Medical Imaging },
  journal = {MDPI Journal of Imaging},
  annote = {Inverse Problems and Imaging},
  volume = {7},
  number = {11},
  pages = {243},
  year = {2021},
  doi = {10.3390/jimaging7110243 }
}


@ARTICLE{Leuschner2021_a4944,
  author = {Johannes Leuschner and Maximilian Schmidt and P.S. Ganguly and V Andriiashen and S.B. Coban and Alexander Denker and D. Bauer and A. Hadjifaradji and K.J. Batenburg and Bolko Maass and M. von Eijnatten},
  title = {Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications},
  journal = {MDPI Journal of Imaging},
  volume = {7},
  number = {3},
  pages = {44},
  year = {2021},
  doi = {10.3390/jimaging7030044}
}





@CONFERENCE{Schmidt2021_ed1f3,
  author = {Maximilian Schmidt and Alexander Denker and Johannes Leuschner},
  title = {The Deep Capsule Prior - advantages through complexity},
  booktitle = {GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021},
  volume = {21},
  number = {1},
  year = {2021},
  doi = {10.1002/pamm.202100166}
}


@CONFERENCE{Denker2020_760c8,
  author = {Alexander Denker and Maximilian Schmidt and Johannes Leuschner and Peter Maaß and Jens Behrmann},
  title = {Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction},
  booktitle = {ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich},
  year = {2020}
}