NETT: Solving Inverse Problems with Deep Neural Networks

28 February 2018

Papers citing "NETT: Solving Inverse Problems with Deep Neural Networks"

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Title
Deep Learning Adapted Acceleration for Limited-view Photoacoustic Computed Tomography Hengrong Lan Jiali Gong Fei Gao 19 8 0 08 Nov 2021
Survey of Deep Learning Methods for Inverse Problems S. Kamyab Zihreh Azimifar Rasool Sabzi Paul Fieguth 21 3 0 07 Nov 2021
Learning convex regularizers satisfying the variational source condition for inverse problems Subhadip Mukherjee A. Bonafonte Mateusz Lajszczak 8 9 0 24 Oct 2021
StyleGAN-induced data-driven regularization for inverse problems Arthur Conmy Subhadip Mukherjee Carola-Bibiane Schönlieb GAN 19 3 0 07 Oct 2021
Deep learning based dictionary learning and tomographic image reconstruction Jevgenija Rudzusika Thomas Koehler Ozan Oktem 54 3 0 26 Aug 2021
Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future Andreas K. Maier Harald Kostler M. Heisig P. Krauss S. Yang MedIm 31 29 0 10 Aug 2021
Regularising Inverse Problems with Generative Machine Learning Models Margaret Duff Neill D. F. Campbell Matthias Joachim Ehrhardt GAN MedIm 29 34 0 22 Jul 2021
Inverse Problem of Nonlinear Schrödinger Equation as Learning of Convolutional Neural Network Yiran Wang Zhen Li 18 2 0 19 Jul 2021
Learning the optimal Tikhonov regularizer for inverse problems Giovanni S. Alberti E. De Vito Matti Lassas Luca Ratti Matteo Santacesaria 25 30 0 11 Jun 2021
End-to-end reconstruction meets data-driven regularization for inverse problems Subhadip Mukherjee M. Carioni Ozan Oktem Carola-Bibiane Schönlieb 23 38 0 07 Jun 2021
Compressed Sensing for Photoacoustic Computed Tomography Using an Untrained Neural Network Hengrong Lan Juze Zhang Changchun Yang Fei Gao 15 1 0 29 May 2021
TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems Hai V. Nguyen T. Bui-Thanh PINN AI4CE 16 9 0 25 May 2021
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks B. Afkham Julianne Chung Matthias Chung 17 42 0 14 Apr 2021
OGGN: A Novel Generalized Oracle Guided Generative Architecture for Modelling Inverse Function of Artificial Neural Networks Mohammad Aaftab Mansi Sharma 30 1 0 08 Apr 2021
Adversarially learned iterative reconstruction for imaging inverse problems Subhadip Mukherjee Ozan Oktem Carola-Bibiane Schönlieb SSL 39 7 0 30 Mar 2021
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 40 225 0 23 Mar 2021
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms M. Holden Marcelo Pereyra K. Zygalakis MedIm 14 27 0 18 Mar 2021
Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization Chen Wei Kexin Lou Zhengyang Wang Mingqi Zhao D. Mantini Quanying Liu 37 21 0 18 Feb 2021
Plug-and-Play gradient-based denoisers applied to CT image enhancement Pasquale Cascarano E. L. Piccolomini E. Morotti Andrea Sebastiani 22 0 0 15 Feb 2021
AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data Mengjie Guo Hengrong Lan C. Yang Fei Gao 19 32 0 22 Jan 2021
Convex Regularization Behind Neural Reconstruction Arda Sahiner Morteza Mardani Batu Mehmet Ozturkler Mert Pilanci John M. Pauly 32 25 0 09 Dec 2020
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data Hao Xu Dongxiao Zhang Nanzhe Wang 19 33 0 24 Nov 2020
Shared Prior Learning of Energy-Based Models for Image Reconstruction Thomas Pinetz Erich Kobler T. Pock Alexander Effland DiffM 19 4 0 12 Nov 2020
Solving Inverse Problems With Deep Neural Networks -- Robustness Included? Martin Genzel Jan Macdonald M. März AAML OOD 24 101 0 09 Nov 2020
Deep learning for biomedical photoacoustic imaging: A review J. Gröhl Melanie Schellenberg Kris K. Dreher Lena Maier-Hein 35 191 0 05 Nov 2020
Towards Reflectivity profile inversion through Artificial Neural Networks J. M. Carmona Loaiza Zamaan Raza 13 11 0 15 Oct 2020
Deep Learning in Photoacoustic Tomography: Current approaches and future directions A. Hauptmann B. Cox 36 130 0 16 Sep 2020
Learned convex regularizers for inverse problems Subhadip Mukherjee Sören Dittmer Zakhar Shumaylov Sebastian Lunz Ozan Oktem Carola-Bibiane Schönlieb 27 79 0 06 Aug 2020
Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction Yunmei Chen Hongcheng Liu X. Ye Qingchao Zhang 56 23 0 22 Jul 2020
Total Deep Variation: A Stable Regularizer for Inverse Problems Erich Kobler Alexander Effland K. Kunisch T. Pock MedIm 19 19 0 15 Jun 2020
Regularization of Inverse Problems by Neural Networks Markus Haltmeier Linh V. Nguyen 30 18 0 06 Jun 2020
On Learned Operator Correction in Inverse Problems Sebastian Lunz A. Hauptmann T. Tarvainen Carola-Bibiane Schönlieb Simon Arridge 6 4 0 14 May 2020
Sparse aNETT for Solving Inverse Problems with Deep Learning D. Obmann Linh V. Nguyen Johannes Schwab Markus Haltmeier 9 7 0 20 Apr 2020
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods Daniel Otero Baguer Johannes Leuschner Maximilian Schmidt 29 186 0 10 Mar 2020
Deep synthesis regularization of inverse problems D. Obmann Johannes Schwab Markus Haltmeier 13 11 0 01 Feb 2020
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm Hao Xu Haibin Chang Dongxiao Zhang AI4CE 25 87 0 21 Jan 2020
Total Deep Variation for Linear Inverse Problems Erich Kobler Alexander Effland K. Kunisch T. Pock 9 89 0 14 Jan 2020
Solving inverse-PDE problems with physics-aware neural networks Samira Pakravan Pouria A. Mistani M. Aragon-Calvo Frédéric Gibou AI4CE 12 50 0 10 Jan 2020
Deep learning architectures for nonlinear operator functions and nonlinear inverse problems Maarten V. de Hoop Matti Lassas C. Wong 19 25 0 23 Dec 2019
Learned SVD: solving inverse problems via hybrid autoencoding Y. Boink Christoph Brune 18 13 0 20 Dec 2019
Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction A. Kofler Markus Haltmeier T. Schaeffter M. Kachelriess M. Dewey Christian Wald C. Kolbitsch 21 1 0 19 Dec 2019
Solving Bayesian Inverse Problems via Variational Autoencoders Hwan Goh Sheroze Sheriffdeen J. Wittmer T. Bui-Thanh BDL 16 38 0 05 Dec 2019
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods Johannes Leuschner Maximilian Schmidt Daniel Otero Baguer Peter Maass 22 32 0 01 Oct 2019
Augmented NETT Regularization of Inverse Problems D. Obmann Linh V. Nguyen Johannes Schwab Markus Haltmeier 16 3 0 08 Aug 2019
Multi-Scale Learned Iterative Reconstruction A. Hauptmann J. Adler Simon Arridge Ozan Oktem 33 37 0 01 Aug 2019
DeepFlow: History Matching in the Space of Deep Generative Models L. Mosser O. Dubrule M. Blunt 32 13 0 14 May 2019
Unsupervised Deep Learning Algorithm for PDE-based Forward and Inverse Problems Leah Bar N. Sochen 6 70 0 10 Apr 2019
An overview of deep learning in medical imaging focusing on MRI A. Lundervold A. Lundervold OOD 6 1,605 0 25 Nov 2018
Random mesh projectors for inverse problems Sidharth Gupta K. Kothari Maarten V. de Hoop Ivan Dokmanić 29 15 0 29 May 2018
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 187 599 0 22 Sep 2016