# Approximate Discrete Radon Transform
[][pypi]
[][docs]
[][tests]
Fast approximate discrete Radon transform for
[NumPy](https://numpy.org/) arrays.
- **Documentation:** https://adrt.readthedocs.io/en/latest/
- **Source Code:** https://github.com/karlotness/adrt
- **Bug Reports:** https://github.com/karlotness/adrt/issues
This library provides an implementation of an approximate discrete
Radon transform (ADRT) and related routines as a Python module
operating on NumPy arrays. Implemented routines include: the forward
ADRT, a back-projection operation, and several inverse transforms. The
package [documentation][docs] contains usage examples, and sample
applications.
## Installation
Install from [PyPI][pypi] using pip:
``` console
$ python -m pip install adrt
```
For further details on installation or building from source, consult
the [documentation][docs].
## References
This implementation is based on descriptions in several publications:
- Martin L. Brady, [A Fast Discrete Approximation Algorithm for the Radon Transform Related Databases][brady98], SIAM Journal on Computing, 27.
- William H. Press, [Discrete Radon transform has an exact, fast inverse and generalizes to operations other than sums along lines][press06], Proceedings of the National Academy of Sciences, 103.
- Donsub Rim, [Exact and fast inversion of the approximate discrete Radon transform from partial data][rim20], Applied Mathematics Letters, 102.
## License
The code in this repository is licensed under a 3-clause BSD license.
See [LICENSE.txt](LICENSE.txt) for the license text.
We also make available several pre-built binary copies of this
software. The binary build for Windows includes additional license
terms for runtime code included as part of the software. Review the
LICENSE.txt file in the binary build package for more information.
[pypi]: https://pypi.org/project/adrt/
[docs]: https://adrt.readthedocs.io/en/latest/
[tests]: https://github.com/karlotness/adrt/actions
[brady98]: https://doi.org/10.1137/S0097539793256673
[press06]: https://doi.org/10.1073/pnas.0609228103
[rim20]: https://doi.org/10.1016/j.aml.2019.106159