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Robustness of data-driven approaches in limited angle tomography
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math.NAcs.LGcs.NA
keywords
angleapproachesdata-drivenlimitedadditionbackprojectioncompareddifficult
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The limited angle Radon transform is notoriously difficult to invert due to its ill-posedness. In this work, we give a mathematical explanation that data-driven approaches can stably reconstruct more information compared to traditional methods like filtered backprojection. In addition, we use experiments based on the U-Net neural network to validate our theory.
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