Fetching the paper…
Reading the bibliography…
This paper introduces a Bayesian framework for image inversion by deriving a probabilistic counterpart to the regularization-by-denoising (RED) paradigm.
J. Douglas and H. H. Rachford, “On the numerical solution of heat conduction problems in two and three space variables,” Trans. Am. Math. Soc. , vol. 82, no. 2, pp. 421–439, 1956
1956
Earlier work this paper cites.
L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: nonlinear phenomena , vol. 60, no. 1-4, pp. 259–268, 1992
1992
Earlier work this paper cites.
D. Geman and C. Yang, “Nonlinear image recovery with half-quadratic regularization,” IEEE Trans. Image Process. , vol. 4, no. 7, pp. 932–946, 1995
1995
Earlier work this paper cites.
G. O. Roberts and O. Stramer, “Langevin diffusions and Metropolis-Hastings algorithms,” Methodology and computing in applied probability , vol. 4, pp. 337–357, 2002
2002
Earlier work this paper cites.
W. C. Karl, “Regularization in image restoration and reconstruction,” in Handbook of image and video processing . Elsevier, 2005, pp. 183–V
2005
Earlier work this paper cites.
A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” in Proc. Int. Conf. Computer Vision Pattern Recognition (CVPR) , vol. 2. Ieee, 2005, pp. 60–65
2005
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 Trans. Image Process. , vol. 16, no. 8, pp. 2080–2095, 2007
2007
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proc. Int. Conf. Computer Vision Pattern Recognition (CVPR) , 2009, pp. 248–255
2009
Earlier work this paper cites.
M. V. Afonso, J. M. Bioucas-Dias, and M. A. Figueiredo, “An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems,” IEEE Trans. Image Process. , vol. 20, no. 3, pp. 681–695, 2010
2010
Earlier work this paper cites.
B. Efron, “Tweedie’s formula and selection bias,” J. Amer. Stat. Soc. , vol. 106, no. 496, pp. 1602–1614, 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 Proc. IEEE Global Conf. Signal Info. Process. (GlobalSIP) . IEEE, 2013, pp. 945–948
2013
Earlier work this paper cites.
D. Revuz and M. Yor, Continuous martingales and Brownian motion . Springer Science & Business Media, 2013, vol. 293
2013
Earlier work this paper cites.
M. D. Fall and É. Barat, “Gibbs sampling methods for Pitman-Yor mixture models,” 2014, Research report. [Online]. Available: https://hal.science/hal-00740770
2014
Earlier work this paper cites.
F. Cao, M. Cai, Y. Tan, and J. Zhao, “Image super-resolution via adaptive ℓ p \ell_{p} ( 0 < p < 1 0<p<1 ) regularization and sparse representation,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 27, no. 7, pp. 1550–1561, 2016
2016
Earlier work this paper cites.
S. H. Chan, X. Wang, and O. A. Elgendy, “Plug-and-play ADMM for image restoration: Fixed-point convergence and applications,” IEEE Trans. Comput. Imag. , vol. 3, no. 1, pp. 84–98, 2016
2016
Cited alongside, same era.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Trans. Image Process. , vol. 26, no. 7, pp. 3142–3155, 2017
2017
Cited alongside, same era.
Y. Romano, M. Elad, and P. Milanfar, “The little engine that could: Regularization by denoising (RED),” SIAM J. Imag. Sci. , vol. 10, no. 4, pp. 1804–1844, 2017
2017
Cited alongside, same era.
E. T. Reehorst and P. Schniter, “Regularization by denoising: Clarifications and new interpretations,” IEEE Trans. Comput. Imag. , vol. 5, no. 1, pp. 52–67, 2018
2018
Cited alongside, same era.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proc. Int. Conf. Computer Vision Pattern Recognition (CVPR) , 2019, pp. 4401–4410
2019
Later among the works it cites.
M. Vono, N. Dobigeon, and P. Chainais, “Asymptotically exact data augmentation: Models, properties, and algorithms,” J. Comput. Graph. Stat. , vol. 30, no. 2, pp. 335–348, 2020
2020
Later among the works it cites.
L. J. Rendell, A. M. Johansen, A. Lee, and N. Whiteley, “Global consensus monte carlo,” J. Comput. Graph. Stat. , vol. 30, no. 2, pp. 249–259, 2020
2020
Later among the works it cites.
M. Terris, A. Repetti, J.-C. Pesquet, and Y. Wiaux, “Building firmly nonexpansive convolutional neural networks,” in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. (ICASSP) . IEEE, 2020, pp. 8658–8662
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
X. Cai, M. Pereyra, and J. D. McEwen, “Uncertainty quantification for radio interferometric imaging –I. Proximal MCMC methods,” Monthly Notices of the Royal Astronomical Society , vol. 480, no. 3, pp. 4154–4169, 2018
2018
Cited alongside, same era.
M. Vono, N. Dobigeon, and P. Chainais, “Sparse Bayesian binary logistic regression using the split-and-augmented Gibbs sampler,” in Proc. IEEE Workshop Mach. Learning for Signal Process. (MLSP) , Aalborg, Denmark, Sept. 2018
2018
Cited alongside, same era.
A. Durmus, E. Moulines, and M. Pereyra, “Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau,” SIAM J. Imag. Sci. , vol. 11, no. 1, pp. 473–506, 2018
2018
Cited alongside, same era.
R. Douc, E. Moulines, P. Priouret, and P. Soulier, Markov Chains , ser. Springer Series in Operations Research and Financial Engineering. Springer International Publishing, 2018
2018
Cited alongside, same era.
E. Ryu, J. Liu, S. Wang, X. Chen, Z. Wang, and W. Yin, “Plug-and-play methods provably converge with properly trained denoisers,” in Proc. Int. Conf. Machine Learning (ICML) . PMLR, 2019, pp. 5546–5557
2019
Cited alongside, same era.
M. Vono, N. Dobigeon, and P. Chainais, “Split-and-augmented Gibbs sampler – Application to large-scale inference problems,” IEEE Trans. Signal Process. , vol. 67, no. 6, pp. 1648–1661, 2019
2019
Cited alongside, same era.
A. Durmus and E. Moulines, “High-dimensional Bayesian inference via the unadjusted Langevin algorithm,” Bernoulli , vol. 25, no. 4A, pp. 2854–2882, 2019
2019
Cited alongside, same era.
K. Zhang, Y. Li, W. Zuo, L. Zhang, L. Van Gool, and R. Timofte, “Plug-and-play image restoration with deep denoiser prior,” IEEE Trans. Patt. Anal. Mach. Intell. , vol. 44, no. 10, pp. 6360–6376, 2021
2021
Later among the works it cites.
R. Cohen, M. Elad, and P. Milanfar, “Regularization by denoising via fixed-point projection (RED-PRO),” SIAM J. Imag. Sci. , vol. 14, no. 3, pp. 1374–1406, 2021
2021
Later among the works it cites.
V. Plassier, M. Vono, A. Durmus, and E. Moulines, “DG-LMC: a turn-key and scalable synchronous distributed MCMC algorithm via Langevin Monte Carlo within Gibbs,” in Proc. Int. Conf. Machine Learning (ICML) . PMLR, 2021, pp. 8577–8587
2021
Later among the works it cites.
S. Hurault, A. Leclaire, and N. Papadakis, “Proximal denoiser for convergent plug-and-play optimization with nonconvex regularization,” in Proc. Int. Conf. Machine Learning (ICML) . PMLR, 2022, pp. 9483–9505
2022
Later among the works it cites.
M. Holden, M. Pereyra, and K. C. Zygalakis, “Bayesian imaging with data-driven priors encoded by neural networks,” SIAM J. Imag. Sci. , vol. 15, no. 2, pp. 892–924, 2022
2022
Later among the works it cites.
R. Laumont, V. D. Bortoli, A. Almansa, J. Delon, A. Durmus, and M. Pereyra, “Bayesian imaging using plug & play priors: when Langevin meets Tweedie,” SIAM J. Imag. Sci. , vol. 15, no. 2, pp. 701–737, 2022
2022
Later among the works it cites.
M. Vono, N. Dobigeon, and P. Chainais, “High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm,” SIAM Rev. , vol. 64, no. 1, pp. 3–56, 2022
2022
Later among the works it cites.
Y. Zhu, K. Zhang, J. Liang, J. Cao, B. Wen, R. Timofte, and L. V. Gool, “Denoising diffusion models for plug-and-play image restoration,” in Int. Conf. Computer Vision Pattern Recognition Workshops (NTIRE) , 2023
2023
Later among the works it cites.
Z. Cai, J. Tang, S. Mukherjee, J. Li, C. B. Schönlieb, and X. Zhang, “NF-ULA: Langevin Monte Carlo with normalizing flow prior for imaging inverse problems,” SIAM J. Imag. Sci. , 2024
2024
Closest in time.
P.-A. Thouvenin, A. Repetti, and P. Chainais, “A distributed Gibbs sampler with hypergraph structure for high-dimensional inverse problems,” J. Comput. Graph. Stat. , 2024
2024
Closest in time.