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Generative models have gained popularity for their potential applications in imaging science, such as image reconstruction, posterior sampling and data sharing.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Navier-stokes, fluid dynamics, and image and video inpainting
Marcelo Bertalmio, Andrea L Bertozzi, and Guillermo Sapiro · 2001
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Theory of remote image formation
Richard E Blahut · 2004
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Linear systems and signals , volume 2
Bhagwandas Pannalal Lathi and Roger A Green · 2005
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Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J Candes, Justin K Romberg, and Terence Tao · 2006
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Image denoising by sparse 3-d transform-domain collaborative filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2007
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Compressed sensing mri
Michael Lustig, David L Donoho, Juan M Santos, and John M Pauly · 2008
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
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Foundations of image science
Harrison H Barrett and Kyle J Myers · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
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Compressive sensing in medical imaging
Christian G Graff and Emil Y Sidky · 2015
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Cited alongside, same era.
Computational radiomics system to decode the radiographic phenotype
Joost JM Van Griethuysen, Andriy Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, Vivek Narayan, Regina GH Beets-Tan, Jean-Christophe Fillion-Robin, Steve Pieper, and Hugo JWL Aerts · 2017
Cited alongside, same era.
Ambientgan: Generative models from lossy measurements
Ashish Bora, Eric Price, and Alexandros G Dimakis · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Prior image-constrained reconstruction using style-based generative models
Varun A Kelkar and Mark Anastasio · 2021
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Compressible latent-space invertible networks for generative model-constrained image reconstruction
Varun A Kelkar, Sayantan Bhadra, and Mark A Anastasio · 2021
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Solving inverse problems in medical imaging with score-based generative models
Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon · 2021
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Deep probabilistic imaging: Uncertainty quantification and multi-modal solution characterization for computational imaging
He Sun and Katherine L Bouman · 2021
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Cryodrgn: reconstruction of heterogeneous cryo-em structures using neural networks
Ellen D Zhong, Tristan Bepler, Bonnie Berger, and Joseph H Davis · 2021
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fastMRI: An open dataset and benchmarks for accelerated MRI, 2018
Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, and Yvonne W. Lui · 2018
Cited alongside, same era.
An introduction to variational autoencoders
Diederik P Kingma, Max Welling, · 2019
Cited alongside, same era.
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Max Daniels, Oscar Leong, Ali Ahmed, and Paul Hand · 2020
Cited alongside, same era.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Pulse: Self-supervised photo upsampling via latent space exploration of generative models
Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin · 2020
Cited alongside, same era.
Deep generative models and inverse problems
Alexandros G Dimakis, Ashish Bora, Dave Van Veen, Ajil Jalal, Sriram Vishwanath, and Eric Price · 2022
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On aliased resizing and surprising subtleties in gan evaluation
Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu · 2022
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Bayesian seismic tomography using normalizing flows
Xuebin Zhao, Andrew Curtis, and Xin Zhang · 2022
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Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
Weimin Zhou, Sayantan Bhadra, Frank J Brooks, Hua Li, and Mark A Anastasio · 2022
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Solving inverse problems with score-based generative priors learned from noisy data
Asad Aali, Marius Arvinte, Sidharth Kumar, and Jonathan I Tamir · 2023
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Ambient diffusion: Learning clean distributions from corrupted data
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G Dimakis, and Adam Klivans · 2023
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Evaluating generative stochastic image models using task-based image quality measures
Varun A Kelkar, Dimitrios S Gotsis, Rucha Deshpande, Frank J Brooks, KC Prabhat, Kyle J Myers, Rongping Zeng, and Mark A Anastasio · 2023
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Conditional injective flows for bayesian imaging
AmirEhsan Khorashadizadeh, Konik Kothari, Leonardo Salsi, Ali Aghababaei Harandi, Maarten de Hoop, and Ivan Dokmanić · 2023
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Challenges in deployable generative AI, ICML 2023 workshop
Swami Sankaranarayanan, Thomas Hartvigsen, Camille Bilodeau, Ryutaro Tanno, Cheng Zhang, Florian Tramer, and Phillip Isola · 2023
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Ideal observer computation by use of markov-chain monte carlo with generative adversarial networks
Weimin Zhou, Umberto Villa, and Mark A Anastasio · 2023
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