O
Ozan Öktem
Researcher at Royal Institute of Technology
Publications - 85
Citations - 3358
Ozan Öktem is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Inverse problem & Iterative reconstruction. The author has an hindex of 19, co-authored 74 publications receiving 2203 citations. Previous affiliations of Ozan Öktem include Okinawa Institute of Science and Technology.
Papers
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Learned Primal-Dual Reconstruction
Jonas Adler,Ozan Öktem +1 more
TL;DR: The Learned Primal-Dual algorithm for tomographic reconstruction accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks.
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Solving ill-posed inverse problems using iterative deep neural networks
Jonas Adler,Ozan Öktem +1 more
TL;DR: In this article, a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators is proposed, which builds on ideas from classical regularisation theory.
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Solving inverse problems using data-driven models
TL;DR: This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.
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Learned Primal-dual Reconstruction
Jonas Adler,Ozan Öktem +1 more
TL;DR: In this article, the learned primal-dual (LPD) algorithm is proposed for tomographic reconstruction, where the proximal operators have been replaced with convolutional neural networks and the algorithm is trained end-to-end, working directly from raw measured data.
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Solving ill-posed inverse problems using iterative deep neural networks
Jonas Adler,Ozan Öktem +1 more
TL;DR: 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 to results in a gradient-like iterative scheme.