Inverse Problems with Learned Forward Operators

arXiv:2311.12528v2 Announce Type: replace-cross
Abstract: Solving inverse problems requires the knowledge of the forward operator, but accurate models can be computationally expensive and hence cheaper variants that do not compromise the reconstruction quality are desired. This chapter reviews reconstruction methods in inverse problems with learned forward operators that follow two different paradigms. The first one is completely agnostic to the forward operator and learns its restriction to the subspace spanned by the training data. The framework of regularisation by projection …

abstract arxiv cs.lg cs.na knowledge math.na operators quality reviews type variants