Welcome to CIL’s documentation!#
The aim of this package is to enable rapid prototyping of optimisation-based reconstruction problems, i.e. defining and solving different optimization problems to enforce different properties on the reconstructed image, while being powerful enough to be employed on real scale problems.
Firstly, it provides a framework to handle acquisition and reconstruction data and metadata; it also provides a basic input/output package to read data from different sources, e.g. Nikon X-Radia, NeXus.
Secondly, it provides an object-oriented framework for defining mathematical operators and functions as well a collection of useful example operators and functions. Both smooth and non-smooth functions can be used.
Further, it provides a number of high-level generic implementations of optimisation algorithms to solve generically formulated optimisation problems constructed from operator and function objects.
Demos and Examples#
A number of demos can be found in the CIL-Demos repository.
For detailed information refer to our articles and the repositories with the code to reproduce the article’s results.
1. Jørgensen JS et al. 2021 Core Imaging Library Part I: a versatile python framework for tomographic imaging https://doi.org/10.1098/rsta.2020.0192 . Phil. Trans. R. Soc. A 20200192. The code to reproduce the article results. https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-I
2. Papoutsellis E et al. 2021 Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography https://doi.org/10.1098/rsta.2020.0193 Phil. Trans. R. Soc. A 20200193. The code to reproduce the article results. https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-II
Cite this work#
If you use this software please consider citing one or both of the articles above.
Software documentation Index#
- Read/ write AcquisitionData and ImageData
- Optimisation framework
- Block Framework
- Test datasets
- Image Quality metrics
- CIL Plugins
- Developers’ Guide