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The recent development of diffusion models has led to significant progress in solving inverse problems by leveraging these models as powerful generative priors.
An empirical bayes approach to statistics
Herbert E Robbins · 1992
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al · 2011
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Tweedie’s formula and selection bias
Bradley Efron · 2011
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Plug-and-play priors for model based reconstruction
Singanallur V Venkatakrishnan, Charles A Bouman, and Brendt Wohlberg · 2013
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Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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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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The little engine that could: Regularization by denoising (red)
Yaniv Romano, Michael Elad, and Peyman Milanfar · 2017
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Ilvr: Conditioning method for denoising diffusion probabilistic models
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Denoising diffusion restoration models
Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song · 2022
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Iterative reconstruction based on latent diffusion model for sparse data reconstruction
Linchao He, Hongyu Yan, Mengting Luo, Kunming Luo, Wang Wang, Wenchao Du, Hu Chen, Hongyu Yang, and Yi Zhang · 2023
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Delta denoising score
Amir Hertz, Kfir Aberman, and Daniel Cohen-Or · 2023
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Improving 3d imaging with pre-trained perpendicular 2d diffusion models
Suhyeon Lee, Hyungjin Chung, Minyoung Park, Jonghyuk Park, Wi-Sun Ryu, and Jong Chul Ye · 2023
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Controlling vision-language models for universal image restoration
Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölund, and Thomas B Schön · 2023
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A variational perspective on solving inverse problems with diffusion models
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Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 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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Zero-shot image restoration using denoising diffusion null-space model
Yinhuai Wang, Jiwen Yu, and Jian Zhang · 2022
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Textir: A simple framework for text-based editable image restoration
Yunpeng Bai, Cairong Wang, Shuzhao Xie, Chao Dong, Chun Yuan, and Zhi Wang · 2023
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∞ \infty -diff: Infinite resolution diffusion with subsampled mollified states
Sam Bond-Taylor and Chris G Willcocks · 2023
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Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael T. McCann, Marc Louis Klasky, and Jong Chul Ye
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Morteza Mardani, Jiaming Song, Jan Kautz, and Arash Vahdat · 2023
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Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall · 2023
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Solving inverse problems with latent diffusion models via hard data consistency
Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, and Liyue Shen · 2023
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Plug-and-play diffusion features for text-driven image-to-image translation
Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel · 2023
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Prompt-tuning latent diffusion models for inverse problems
Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, and Mauricio Delbracio · 2024
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Energy-based cross attention for bayesian context update in text-to-image diffusion models
Geon Yeong Park, Jeongsol Kim, Beomsu Kim, Sang Wan Lee, and Jong Chul Ye · 2024
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Solving linear inverse problems provably via posterior sampling with latent diffusion models
Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alex Dimakis, and Sanjay Shakkottai · 2024
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