ToMoBAR's documentation

ToMoBAR (cite [CT2020], [SX2022]) is a Python library of direct and model-based regularised iterative reconstruction algorithms with a plug-and-play capability. ToMoBAR offers you a selection of various data models and regularisers resulting in complex objectives for tomographic reconstruction. ToMoBAR can operate in GPU device-to-device fashion on CuPy arrays therefore ensuring a better computational efficiency. With GPU device controlling API exposed it can also support multi-GPU parallel computing.

Although ToMoBAR does offer a variety of reconstruction methods, the FISTA algorithm [BT2009]_ specifically provides various useful modifications, e.g.: convergence acceleration with ordered-subsets, different data fidelities: PWLS, Kullback-Leibler, Huber, Group-Huber [PM2015], Students't [KAZ1_2017], and SWLS [HOA2017]_ to deal with noise and reconstruction artefacts (rings, streaks). Together with the regularisers from the CCPi-Regularisation toolkit [KAZ2019]_ one can construct up to a hundred of complex combinations for the objective function.

.. figure:: _static/recsFISTA_stud.png :scale: 85 % :alt: ToMoBAR in action