A neural operator view on U-Nets for inverse imaging problems
Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging. Yet, very few works have studied their behavior in the limit that turns the discretized ill-conditioned problems into truly ill-posed ones, i.e., for an increasing resolution of the discretization. In this work, we review common approaches to neural operator learning in architectures that resemble a U-Net, one of the most common classical architectures for inverse imaging problems. We discuss advantages and drawbacks of the respective approaches, consider a 1D toy example for improved interpretability, and present extensive numerical experiments on how differ...
| Authors | Alexander Auras, Martin Burger, Samira Kabri et al. |
|---|---|
| Primary category | math.NA |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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