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We present TRex, a flexible and robust Tomographic Reconstruction frameworkusing proximal algorithms. We provide an overview and perform an experimentalcomparison between the famous iterative reconstruction methods in terms ofreconstruction quality in sparse view situations. We then derive the proximaloperators for the four best methods. We show the flexibility of our frameworkby deriving solvers for two noise models: Gaussian and Poisson; and by pluggingin three powerful regularizers. We compare our framework to state of the artmethods, and show superior quality on both synthetic and real datasets.

TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications