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.
For more details and an introduction into the inner workings of ODL, please refer to the documentation.
- Well-tested data containers based on NumPy or CUDA allow high performance computing with minimal effort.
- A versatile and efficient library of optimization routines for smooth and non-smooth problems, such as CGLS, BFGS, PDHG and Douglas-Rachford splitting.
- Support for tomographic imaging with a unified geometry representation and bindings to external libraries for efficient computation of projections and back-projections.
- And much more, including support for deep learning libraries, figures of merits, phantom generation, data handling, etc.
Installing ODL should be as easy as
conda install -c odlgroup odl
or
pip install odl
For more detailed instructions, check out the Installation guide.
ODL is compatible with Python 2/3 and all major platforms (GNU/Linux / Mac / Windows).
- ODL Documentation
- Installation guide
- Getting Started
- Code Examples
- API reference
- ODL Course Material
This is an incomplete list of articles and projects using ODL. If you want to add your project to the list, contact the maintainers or file a pull request.
| Article | Code |
|---|---|
| Learning to solve inverse problems using Wasserstein loss. NIPS OMT Workshop 2017. arXiv | |
| Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. Article | |
| Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. arXiv | |
| Learned Primal-Dual Reconstruction. arXiv, blog | |
| Indirect Image Registration with Large Diffeomorphic Deformations. arXiv | |
| High-level algorithm prototyping: an example extending the TVR-DART algorithm. DGCI, 2017. DOI | |
| GPUMCI, a flexible platform for x-ray imaging on the GPU. Fully3D, 2017 | |
| Spectral CT reconstruction with anti-correlated noise model and joint prior. Fully3D, 2017 | |
| Solving ill-posed inverse problems using iterative deep neural networks. Inverse Problems, 2017 arXiv, DOI | |
| Total variation regularization with variable Lebesgue prior. arXiv | |
| Generalized Sinkhorn iterations for regularizing inverse problems using optimal mass transport. SIAM Journal on Imaging Sciences, 2017. arXiv, DOI | |
| A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images. CMBEBIH, 2017 | |
| The MAX IV imaging concept. Article | |
| Shape Based Image Reconstruction Using Linearized Deformations. Inverse Problems, 2017. DOI |
| Project | Code |
|---|---|
| Multigrid CT reconstruction | |
| Inverse problems over Lie groups | |
| Bindings for the EMRecon package for PET | |
| ADF-STEM reconstruction using nuclear norm regularization |
Mozilla Public License version 2.0 or later. See the LICENSE file.
Development of ODL started in 2014 as part of the project "Low complexity image reconstruction in medical imaging” by Ozan Öktem (@ozanoktem), Jonas Adler (@adler-j) and Holger Kohr (@kohr-h). Several others have made significant contributions, see the contributors list.
To contact the developers either open an issue on the issue tracker or send an email to odl@math.kth.se.
ODL has primarily been developed at KTH Royal Institute of Technology, Stockholm and Centrum Wiskunde & Informatica (CWI), Amsterdam. It is financially supported by the Swedish Foundation for Strategic Research as part of the project "Low complexity image reconstruction in medical imaging".
Some development time has also been financed by Elekta.