Solving ill-posed inverse problems using iterative deep neural networks

13 Apr 2017 Jonas Adler Ozan Öktem

We propose a partially learned approach for the solution of ill posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularization theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularizing functional... (read more)

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