Data-Driven Interpolation for Super-Scarce X-Ray Computed Tomography

@article{Valat2022DataDrivenIF,
  title={Data-Driven Interpolation for Super-Scarce X-Ray Computed Tomography},
  author={Emilien Valat and Katayoun Farrahi and Thomas Blumensath},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.07888}
}
—We address the problem of reconstructing X-Ray tomographic images from scarce measurements by interpolating missing acquisitions using a self-supervised approach. To do so, we train shallow neural networks to combine two neighbouring acquisitions into an estimated measurement at an intermediate angle. This procedure yields an enhanced sequence of measurements that can be reconstructed using standard methods, or further enhanced using regularisation approaches. Unlike methods that improve the… 

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