Learned iterative reconstruction networks can be uniformly described as operator learning: the unrolled architecture fixes how to compute while the loss and data fix what to compute; for nonlinear inverse problems the update direction matters most.
Unsupervised approaches based on optimal transport and convex anal- ysis for inverse problems in imaging
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Learned iterative networks: An operator learning perspective
Learned iterative reconstruction networks can be uniformly described as operator learning: the unrolled architecture fixes how to compute while the loss and data fix what to compute; for nonlinear inverse problems the update direction matters most.