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Over the past decade, Plug-and-Play (PnP) has become a popular method for reconstructing images using a modular framework consisting of a forward and prior model.
S. Geman and D. Geman, “Stochastic relaxation, gibbs distributions, and the bayesian restoration of images,” IEEE Trans. on Pattern Analysis and Machine Intelligence , vol. PAMI-6, no. 6, pp. 721–741, 1984
1984
Earlier work this paper cites.
U. Grenander and M. Miller, “Stochastic relaxation, gibbs distributions, and the bayesian restoration of images,” Journal of the Royal Statistical Society B , vol. 56, no. 4, pp. 549–581, 1994
1994
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Transactions on Image Processing , vol. 16, no. 8, pp. 2080–2095, 2007
2007
Earlier work this paper cites.
P. Vincent, “A connection between score matching and denoising autoencoders,” Neural Computation , vol. 23, no. 7, p. 1661–1674, 2011
2011
Earlier work this paper cites.
S. V. Venkatakrishnan, C. A. Bouman, and B. Wohlberg, “Plug-and-play priors for model based reconstruction,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2013 . IEEE, 2013, pp. 945–948
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems (NeurIPS , vol. 27, 2014
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in International Conference on Learning Representations (ICLR) , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Sreehari, S. V. Venkatakrishnan, B. Wohlberg, G. T. Buzzard, L. F. Drummy, J. P. Simmons, and C. A. Bouman, “Plug-and-play priors for bright field electron tomography and sparse interpolation,” IEEE Transactions on Computational Imaging , vol. 2, no. 4, pp. 408–423, Dec 2016
2016
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
B. T. Feng, J. Smith, M. Rubinstein, H. Chang, K. L. Bouman, and W. T. Freeman, “Score-based diffusion models as principled priors for inverse imaging,” 2017
2017
Cited alongside, same era.
G. T. Buzzard and C. A. B. Stanley H. Chan, Suhas Sreehari, “Plug-and-play unplugged: Optimization-free reconstruction using consensus equilibrium,” SIAM Journal on Imaging Sciences , vol. 11, no. 3, pp. 2001–2020, 2018
2018
Cited alongside, same era.
S. Majee, T. Balke, C. A. J. Kemp, G. T. Buzzard, and C. A. Bouman, “Multi-slice fusion for sparse-view and limited-angle 4d ct reconstruction,” IEEE Transactions on Computational Imaging , vol. 7, 2021
2021
Later among the works it cites.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Robust compressed sensing MRI with deep generative priors,” in International Conference on Learning Representations (ICLR) , 2021
2021
Later among the works it cites.
A. Jalal, M. Arvinte, G. Daras, E. Price, A. G. Dimakis, and J. I. Tamir, “Robust compressed sensing MRI with deep generative priors,” in Advances in Neural Information Processing Systems (NeurIPS) , 2021
2021
Later among the works it cites.
Y. Song, L. Shen, L. Xing, and S. Ermon, “Solving inverse problems in medical imaging with score-based generative models,” in International Conference on Learning Representations (ICLR) , 2022
2022
Later among the works it cites.
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Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” in Advances in Neural Information Processing Systems (NeurIPS) , 2019, pp. 11 895–11 907
2019
Cited alongside, same era.
Y. Song and S. Ermon, “Improved techniques for training score-based generative models,” in Advances in Neural Information Processing Systems (NeurIPS) , 2020
2020
Cited alongside, same era.
S. D. Team, “Super-Voxel Model Based Iterative Reconstruction (SVMBIR),” Software library available from https://github.com/cabouman/svmbir , 2020
2020
Cited alongside, same era.
C. A. Bouman, Foundations of Computational Imaging: A Model Based Approach . Philadelphia: Society for Industrial and Applied Mathematics, 2022
2022
Later among the works it cites.
C. J. Pellizzari, T. J. Bate, K. P. Donnelly, G. T. Buzzard, C. A. Bouman, and M. F. Spencer, “Coherent plug-and-play artifact removal: Physics-based deep learning for imaging through aberrations,” Optics and Lasers in Engineering , vol. 164, 2023
2023
Closest in time.
H. Chung, J. Kim, M. T. Mccann, M. L. Klasky, and J. C. Ye, “Diffusion posterior sampling for general noisy inverse problems,” in International Conference on Learning Representations (ICLR) , 2023
2023
Closest in time.