Operator Discretization Library (ODL) is a Python library that enables research in inverse problems on realistic or real data. The framework allows to encapsulate a physical model into an Operator that can be used like a mathematical object in, e.g., optimization methods. Furthermore, ODL makes it easy to experiment with reconstruction methods and optimization algorithms for variational regularization, all without sacrificing performance.

References in zbMATH (referenced in 18 articles )

Showing results 1 to 18 of 18.
Sorted by year (citations)

  1. Heaton, Howard; Fung, Samy Wu; Lin, Alex Tong; Osher, Stanley; Yin, Wotao: Wasserstein-based projections with applications to inverse problems (2022)
  2. Bubba, Tatiana A.; Galinier, Mathilde; Lassas, Matti; Prato, Marco; Ratti, Luca; Siltanen, Samuli: Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography (2021)
  3. Heaton, Howard; Wu Fung, Samy; Gibali, Aviv; Yin, Wotao: Feasibility-based fixed point networks (2021)
  4. Jakob S. Jørgensen, Evelina Ametova, Genoveva Burca, Gemma Fardell, Evangelos Papoutsellis, Edoardo Pasca, Kris Thielemans, Martin Turner, Ryan Warr, William R. B. Lionheart, Philip J. Withers: Core Imaging Library - Part I: a versatile Python framework for tomographic imaging (2021) arXiv
  5. Obmann, Daniel; Schwab, Johannes; Haltmeier, Markus: Deep synthesis network for regularizing inverse problems (2021)
  6. Pouchol, Camille; Verdier, Olivier: Statistical model and ML-EM algorithm for emission tomography with known movement (2021)
  7. Yamaev, A. V.; Chukalina, M. V.; Nikolaev, D. P.; Sheshkus, A. V.; Chulichkov, A. I.: Neural network for data preprocessing in computed tomography (2021)
  8. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  9. Lederman, Roy R.; Andén, Joakim; Singer, Amit: Hyper-molecules: on the representation and recovery of dynamical structures for applications in flexible macro-molecules in cryo-EM (2020)
  10. Pouchol, Camille; Verdier, Olivier: The ML-EM algorithm in continuum: sparse measure solutions (2020)
  11. Guo, Yan; Aveyard, Richard; Rieger, Bernd: A multichannel cross-modal fusion framework for electron tomography (2019)
  12. Lee, G. R.; Gommers, R.; Waselewski, F.; Wohlfahrt, K.; O’Leary, A.: PyWavelets: A Python package for wavelet analysis (2019) not zbMATH
  13. Soubies, Emmanuel; Soulez, Ferréol; McCann, Michael T.; Pham, Thanh-an; Donati, Laurène; Debarre, Thomas; Sage, Daniel; Unser, Michael: Pocket guide to solve inverse problems with GlobalBioim (2019)
  14. Chambolle, Antonin; Ehrhardt, Matthias J.; Richtárik, Peter; Schönlieb, Carola-Bibiane: Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications (2018)
  15. Zickert, Gustav; Maretzke, Simon: Cryogenic electron tomography reconstructions from phaseless data (2018)
  16. Karlsson, Johan; Ringh, Axel: Generalized Sinkhorn iterations for regularizing inverse problems using optimal mass transport (2017)
  17. Öktem, Ozan; Chen, Chong; Domaniç, Nevzat Onur; Ravikumar, Pradeep; Bajaj, Chandrajit: Shape-based image reconstruction using linearized deformations (2017)
  18. Ringh, Axel; Zhuge, Xiaodong; Palenstijn, Willem Jan; Batenburg, Kees Joost; Öktem, Ozan: High-level algorithm prototyping: an example extending the TVR-DART algorithm (2017)

Further publications can be found at: https://odlgroup.github.io/odl/refs.html