In sparse X-ray Computed Tomography, the radiation dose to the patient is lowered by measuring fewer projection views compared to a standard protocol. In this work we investigate a hybrid approach combining shearlet representation with deep learning for reconstruction of sparse-view X-ray computed tomography. The proposed method is hybrid in that it reconstructs the parts that can provably be retrieved by utilizing a model-based approach, and it in-paints the parts that provably cannot through a learning-based approach. In doing so, we attempt to benefit from the best aspects of model- and learning-based methods. We demonstrate first promising results on publicly available data.
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