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.

For more details and an introduction into the inner workings of ODL, please refer to the documentation. The features include:

• 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.

https://github.com/odlgroup/odl 

https://odlgroup.github.io/odl/