• We utilize the flexible numerical optimization framework of proximal algorithms like the ADMM method. 
• We developed a high quality solver based on the SART algorithm and combined it with several regularizers that achieved better results that state-of-the-art. 
• We extended a structure tensor-based regularizer to 3D and applied it to specific structures such as thin sheets and fibers.
• We also developed solvers for different noise models, and advanced structure tensor-based regularizers for handling special structures in the scanned objects. 
• We compared different proximal solvers for the tomography problem, including SART, ART, BICAV, and Ordered Subsets. 
• We developed solvers for the Gaussian and Poisson noises. 
• We performed extensive experiments on 2D synthetic and real datasets.

• TRex toolbox for X-Ray CT Reconstruction.

• Guangming Zang, Mohamed Aly, Peter Wonka, and Wolfgang Heidrich. Super-resolution CT Reconstruction. In preparation, 2017.
• Mohamed Aly, Guangming Zang, Wolfgang Heidrich, and Peter Wonka. TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications. arXiv preprint arXiv:1606.03601, 2016, [pdf]

Acknowledgements