Fetching the paper…
Reading the bibliography…
Solving ill-posed inverse problems requires careful formulation of prior beliefs over the signals of interest and an accurate description of their manifestation into noisy measurements.
Reverse-time diffusion equation models
Brian DO Anderson · 1982
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
A wavelet tour of signal processing
Stéphane Mallat · 1999
Earlier work this paper cites.
Image denoising with block-matching and 3d filtering
Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian · 2006
Earlier work this paper cites.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Non-local image dehazing
Dana Berman, Shai Avidan, et al · 2016
Earlier work this paper cites.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Earlier work this paper cites.
Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image
Jian Yang, Jingfan Fan, Danni Ai, Xuehu Wang, Yongchang Zheng, Songyuan Tang, and Yongtian Wang · 2016
Earlier work this paper cites.
Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
Earlier work this paper cites.
The perception-distortion tradeoff
Yochai Blau and Tomer Michaeli · 2018
Earlier work this paper cites.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Earlier work this paper cites.
Synchronous and asynchronous radar interference mitigation
Faruk Uysal · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Earlier work this paper cites.
Progressive image deraining networks: A better and simpler baseline
Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng · 2019
Cited alongside, same era.
The generalized contrast-to-noise ratio: A formal definition for lesion detectability
Alfonso Rodriguez-Molares, Ole Marius Hoel Rindal, Jan D’hooge, vein-Erik Måsøy, Andreas Austeng, Muyinatu A. Lediju Bell, and Hans Torp · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 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.
Subspace diffusion generative models
Bowen Jing, Gabriele Corso, Renato Berlinghieri, and Tommi Jaakkola · 2022
Later among the works it cites.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Later among the works it cites.
Denoising diffusion restoration models
Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song · 2022
Later among the works it cites.
Understanding diffusion models: A unified perspective
Calvin Luo · 2022
Later among the works it cites.
Diffusion model based posterior sampling for noisy linear inverse problems
Xiangming Meng and Yoshiyuki Kabashima · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Snips: Solving noisy inverse problems stochastically
Bahjat Kawar, Gregory Vaksman, and Michael Elad · 2021
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2021
Cited alongside, same era.
Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
Cited alongside, same era.
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Zero-shot image restoration using denoising diffusion null-space model
Yinhuai Wang, Jiwen Yu, and Jian Zhang · 2022
Later among the works it cites.
Deep unfolding with normalizing flow priors for inverse problems
Xinyi Wei, Hans van Gorp, Lizeth Gonzalez-Carabarin, Daniel Freedman, Yonina C Eldar, and Ruud JG van Sloun · 2022
Later among the works it cites.
Score-based diffusion models as principled priors for inverse imaging
Berthy T Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L Bouman, and William T Freeman · 2023
Closest in time.
User-defined event sampling and uncertainty quantification in diffusion models for physical dynamical systems
Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, and Leonardo Zepeda-Núñez · 2023
Closest in time.
Image restoration with mean-reverting stochastic differential equations
Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölund, and Thomas B Schön · 2023
Closest in time.
A variational perspective on solving inverse problems with diffusion models
Morteza Mardani, Jiaming Song, Jan Kautz, and Arash Vahdat · 2023
Closest in time.
Pseudoinverse-guided diffusion models for inverse problems
Jiaming Song, Arash Vahdat, Morteza Mardani, and Jan Kautz · 2023
Closest in time.
Deep equilibrium diffusion restoration with parallel sampling
Jiezhang Cao, Yue Shi, Kai Zhang, Yulun Zhang, Radu Timofte, and Luc Van Gool · 2024
Closest in time.
Inference-time diffusion model distillation
Geon Yeong Park, Sang Wan Lee, and Jong Chul Ye · 2024
Closest in time.
Sequential Posterior Sampling with Diffusion Models
Tristan S. W. Stevens, Oisín Nolan, Jean-Luc Robert, and Ruud J.G. van Sloun · 2025
Closest in time.