ODL

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 10 articles )

Showing results 1 to 10 of 10.
Sorted by year (citations)

  1. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  2. Pouchol, Camille; Verdier, Olivier: The ML-EM algorithm in continuum: sparse measure solutions (2020)
  3. Guo, Yan; Aveyard, Richard; Rieger, Bernd: A multichannel cross-modal fusion framework for electron tomography (2019)
  4. Lee, G. R.; Gommers, R.; Waselewski, F.; Wohlfahrt, K.; O’Leary, A.: PyWavelets: A Python package for wavelet analysis (2019) not zbMATH
  5. 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)
  6. Chambolle, Antonin; Ehrhardt, Matthias J.; Richtárik, Peter; Schönlieb, Carola-Bibiane: Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications (2018)
  7. Zickert, Gustav; Maretzke, Simon: Cryogenic electron tomography reconstructions from phaseless data (2018)
  8. Karlsson, Johan; Ringh, Axel: Generalized Sinkhorn iterations for regularizing inverse problems using optimal mass transport (2017)
  9. Öktem, Ozan; Chen, Chong; Domaniç, Nevzat Onur; Ravikumar, Pradeep; Bajaj, Chandrajit: Shape-based image reconstruction using linearized deformations (2017)
  10. 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