References

CT2020

D. Kazantsev and N. Wadeson. 2020, Tomographic MOdel-BAsed Reconstruction (ToMoBAR) software for high resolution synchrotron X-ray tomography, CT Meeting 2020. Download here.

SX2022

D. Kazantsev, N. Wadeson, M. Basham, 2022. High performance Savu software for fast 3D model-based iterative reconstruction of large data at Diamond Light Source. SoftwareX, 19, p.101157.

BT2009

A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183–202, 2009.

PM2015

P. Paleo and A. Mirone 2015. Ring artifacts correction in compressed sensing tomographic reconstruction. Journal of synchrotron radiation, 22(5), pp.1268-1278.

HOA2017

H. Om Aggrawal et al. 2017. A Convex Reconstruction Model for X-ray tomographic Imaging with Uncertain Flat-fields”, IEEE Transactions on Computational Imaging.

KAZ1_2017

D. Kazantsev et al. 2017. A Novel Tomographic Reconstruction Method Based on the Robust Student’s t Function For Suppressing Data Outliers. IEEE TCI, 3(4), pp.682-693.

KAZ2019

D. Kazantsev et al. 2019. CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms. SoftwareX, 9, pp.317-323.

GUO2018

E. Guo et al. 2018. The influence of nanoparticles on dendritic grain growth in Mg alloys. Acta Materialia.

KAZ2017

D. Kazantsev et al. 2017. Model-based iterative reconstruction using higher-order regularization of dynamic synchrotron data. Measurement Science and Technology, 28(9), p.094004.