Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. (arXiv:2206.07050v1 [eess.IV])

Web: http://arxiv.org/abs/2206.07050

This work is concerned with the following fundamental question in scientific
machine learning: Can deep-learning-based methods solve noise-free inverse
problems to near-perfect accuracy? Positive evidence is provided for the first
time, focusing on a prototypical computed tomography (CT) setup. We demonstrate
that an iterative end-to-end network scheme enables reconstructions close to
numerical precision, comparable to classical compressed sensing strategies. Our
results build on our winning submission to the recent AAPM DL-Sparse-View CT
Challenge. Its goal was to identify the state-of-the-art …

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