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Deep neural network approaches to inverse imaging problems have produced impressive results in the last few years.
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“Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems”
Tim Meinhardt, Michael Moeller, Caner Hazirbas and Daniel Cremers · 2017
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Paul Hand and Vladislav Voroninski · 2019
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Shady Hussein, Tom Tirer and Raja Giryes · 2019
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“Algorithmic guarantees for inverse imaging with untrained network priors”
Gauri Jagatap and Chinmay Hegde · 2019
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Diederik. Kingma and Max Welling · 2019
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Avisek Lahiri, Arnav Jain, Divyasri Nadendla and Prabir Biswas · 2019
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Qi Lei, Ajil Jalal, Inderjit. Dhillon and Alexandros. Dimakis · 2019
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Guang Yang et al · 2017
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Raymond. Yeh et al · 2017
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Jun Zhu, Taesung Park, Phillip Isola and Alexei. Efros · 2017
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Jonas Adler and Ozan “”Oktem · 2018
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Florian Knoll et al · 2020
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“Sparse Anett for Solving Inverse Problems with Deep Learning”
Daniel Obmann, Linh Nguyen, Johannes Schwab and Markus Haltmeier · 2020
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“Unpaired Deep Learning for Accelerated MRI Using Optimal Transport Driven CycleGAN”
Gyutaek Oh et al · 2020
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“Denoising Score-Matching for Uncertainty Quantification in Inverse Problems”
Zaccharie Ramzi et al · 2020
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“Optimal Transport Structure of CycleGAN for Unsupervised Learning for Inverse Problems”
Byeongsu Sim, Gyutaek Oh and Jong Ye · 2020
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“Improved techniques for training score-based generative models”
Yang Song and Stefano Ermon · 2020
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“Deep Image Prior”
Dmitry Ulyanov, Andrea Vedaldi and Victor Lempitsky · 2020
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“Intermediate Layer Optimization for Inverse Problems using Deep Generative Models”
Giannis Daras, Joseph Dean, Ajil Jalal and Alexandros. Dimakis · 2021
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