Library for testing and comparing deep learning based methods for inverse problems, written in python.
See the documentation.
The project is also available on PyPI.
One main goal of this library is to provide public standard datasets suitable for deep learning. Currently, the following datasets are included:
'ellipses': A typical synthetical CT dataset with ellipse phantoms.'lodopab': The public LoDoPaB-CT dataset, based on real CT reconstructions from the public LIDC-IDRI dataset. See also theLoDoPaBDatasetclass defined in lodopab_dataset.
These datasets can be accessed by calling dival.get_standard_dataset(name).
Note on astra and CUDA: The CT datasets come with a ray_trafo attribute providing
the forward operator.
There are different backend implementations for it, the default is 'astra_cuda'.
This requires both the astra-toolbox and
a CUDA-enabled GPU being available.
In order to use the (slow) scikit-image backend instead, you can pass impl='skimage' to get_standard_dataset. If astra is
available but CUDA is not, impl='astra_cpu' is preferred.
The latest development version of astra can be installed with
conda install astra-toolbox -c astra-toolbox/label/dev.
We would like to include more reconstruction methods. If you know of classical or state-of-the-art methods that should not be missing in our library, please let us know!
Also, bug reports and suggestions on improving our library are welcome. Please file an issue for such a purpose.