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With the rapid development of diffusion models and flow-based generative models, there has been a surge of interests in solving noisy linear inverse problems, e.g., super-resolution, deblurring, denoising, colorization, etc, with generative models.
A statistical perspective on ill-posed inverse problems
Finbarr O’Sullivan · 1986
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An empirical bayes approach to statistics
Herbert E Robbins · 1992
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Image inpainting
Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester · 2000
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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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A review of image denoising algorithms, with a new one
Antoni Buades, Bartomeu Coll, and Jean-Michel Morel · 2005
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Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
Emmanuel J Candès, Justin Romberg, and Terence Tao · 2006
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Image deblurring with blurred/noisy image pairs
Lu Yuan, Jian Sun, Long Quan, and Heungyeung Shum · 2007
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An introduction to compressive sampling
Emmanuel J Candès and Michael B Wakin · 2008
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Compressed sensing and robust recovery of low rank matrices
Maryam Fazel, E Candes, Benjamin Recht, and P Parrilo · 2008
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Denoising diffusion restoration models
Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song · 2022
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Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Pseudoinverse-guided diffusion models for inverse problems
Jiaming Song, Arash Vahdat, Morteza Mardani, and Jan Kautz · 2022
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Solving linear inverse problems using the prior implicit in a denoiser
Zahra Kadkhodaie and Eero P Simoncelli · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Cited alongside, same era.
Ilvr: Conditioning method for denoising diffusion probabilistic models
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Stochastic solutions for linear inverse problems using the prior implicit in a denoiser
Zahra Kadkhodaie and Eero Simoncelli · 2021
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Snips: Solving noisy inverse problems stochastically
Bahjat Kawar, Gregory Vaksman, and Michael Elad · 2021
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Zero-shot image restoration using denoising diffusion null-space model
Yinhuai Wang, Jiwen Yu, and Jian Zhang · 2022
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Stochastic interpolants: A unifying framework for flows and diffusions
Michael S Albergo, Nicholas M Boffi, and Eric Vanden-Eijnden · 2023
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Quantized compressed sensing with score-based generative models
Xiangming Meng and Yoshiyuki Kabashima · 2023
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Training-free linear image inversion via flows
Ashwini Pokle, Matthew J Muckley, Ricky TQ Chen, and Brian Karrer · 2023
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie · 2024
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Qcs-sgm+: Improved quantized compressed sensing with score-based generative models
Xiangming Meng and Yoshiyuki Kabashima · 2024
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