Robustness of data-driven approaches in limited angle tomography
This addresses the challenge of image reconstruction in medical or industrial imaging for practitioners, but it appears incremental as it builds on existing data-driven methods.
The paper tackled the ill-posed problem of limited angle tomography inversion and found that data-driven approaches can stably reconstruct more information than traditional methods like filtered backprojection, as validated through U-Net experiments.
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.