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Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers.
R. T. Rockafellar, Convex Analysis . Princeton, NJ: Princeton Univ. Press, 1970, ch. Conjugate Saddle-Functions and Minimax Theorems, pp. 388–398
1970
Earlier work this paper cites.
J. Eckstein and D. P. Bertsekas, “On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators,” Mathematical Programming , vol. 55, pp. 293–318, 1992
1992
Earlier work this paper cites.
R. T. Rockafellar and R. Wets, Variational Analysis . Springer, 1998
1998
Earlier work this paper cites.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proc. IEEE Int. Conf. Comp. Vis. (ICCV) , Vancouver, Canada, July 7-14, 2001, pp. 416–423
2001
Earlier work this paper cites.
2002
Earlier work this paper cites.
M. A. T. Figueiredo and R. D. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Trans. Image Process. , vol. 12, no. 8, pp. 906–916, August 2003
2003
Earlier work this paper cites.
I. Daubechies, M. Defrise, and C. D. Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. , vol. 57, no. 11, pp. 1413–1457, November 2004
2004
Earlier work this paper cites.
J. Bect, L. Blanc-Feraud, G. Aubert, and A. Chambolle, “A ℓ 1 \ell_{1} -unified variational framework for image restoration,” in Proc. ECCV , Springer, Ed., vol. 3024, New York, 2004, pp. 1–13
2004
Earlier work this paper cites.
A. Chambolle, “An algorithm for total variation minimization and applications,” Journal of Mathematical Imaging and Vision , vol. 20, no. 1, pp. 89–97, 2004
2004
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex Optimization . Cambridge Univ. Press, 2004
2004
Earlier work this paper cites.
Y. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course . Kluwer Academic Publishers, 2004
2004
Earlier work this paper cites.
2005
Earlier work this paper cites.
E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory , vol. 52, no. 2, pp. 489–509, February 2006
2006
Earlier work this paper cites.
D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory , vol. 52, no. 4, pp. 1289–1306, April 2006
2006
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. 16, pp. 2080–2095, August 2007
2007
Earlier work this paper cites.
H. H. Bauschke, R. Goebel, Y. Lucet, and X. Wang, “The proximal average: Basic theory,” SIAM J. Optim. , vol. 19, no. 2, pp. 766–785, 2008
2008
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, “Subgradients,” April 2008, class notes for Convex Optimization II. http://see.stanford.edu/materials/lsocoee364b/01-subgradients_notes.pdf
2008
Earlier work this paper cites.
A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sciences , vol. 2, no. 1, pp. 183–202, 2009
2009
Earlier work this paper cites.
A. Beck and M. Teboulle, “Fast gradient-based algorithm for constrained total variation image denoising and deblurring problems,” IEEE Trans. Image Process. , vol. 18, no. 11, pp. 2419–2434, November 2009
2009
Earlier work this paper cites.
M. V. Afonso, J. M.Bioucas-Dias, and M. A. T. Figueiredo, “Fast image recovery using variable splitting and constrained optimization,” IEEE Trans. Image Process. , vol. 19, no. 9, pp. 2345–2356, September 2010
2010
Earlier work this paper cites.
M. K. Ng, P. Weiss, and X. Yuan, “Solving constrained total-variation image restoration and reconstruction problems via alternating direction methods,” SIAM J. Sci. Comput. , vol. 32, no. 5, pp. 2710–2736, August 2010
2010
Earlier work this paper cites.
B. Recht, M. Fazel, and P. A. Parrilo, “Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization,” SIAM Rev. , vol. 52, no. 3, pp. 471–501, 2010
2010
Earlier work this paper cites.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends in Machine Learning , vol. 3, no. 1, pp. 1–122, 2011
2011
Earlier work this paper cites.
D. P. Bertsekas, “Incremental proximal methods for large scale convex optimization,” Math. Program. Ser. B , vol. 129, pp. 163–195, 2011
2011
Earlier work this paper cites.
H. Wang and A. Banerjee, “Online alternating direction method,” in Proc. 29th Int. Conf. Machine Learning (ICML) , Edinburgh, Scotland, UK, June 26-July 1, 2012, pp. 1699–1706
2012
Earlier work this paper cites.
S. Ramani and J. A. Fessler, “A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction,” IEEE Trans. Med. Imaging , vol. 31, no. 3, pp. 677–688, March 2012
2012
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 Process. and INf. Process. (GlobalSIP) , 2013
2013
Cited alongside, same era.
H. Ouyang, N. He, L. Q. Tran, and A. Gray, “Stochastic alternating direction method of multipliers,” in Proc. 30th Int. Conf. Machine Learning (ICML) , Atlanta, GA, USA, 16-21 June, 2013, pp. 80–88
2013
Cited alongside, same era.
T. Suzuki, “Dual averaging and proximal gradient descent for online alternating direction multiplier method,” in Proc. 30th Int. Conf. Machine Learning (ICML) , Atlanta, GA, USA, Jun. 2013, pp. 392–400
2013
Cited alongside, same era.
M. Almeida and M. Figueiredo, “Deconvolving images with unknown boundaries using the alternating direction method of multipliers,” IEEE Trans. Image Process. , vol. 22, no. 8, pp. 3074–3086, August 2013
2013
Cited alongside, same era.
G. T. Buzzard, S. H. Chan, S. Sreehari, and C. A. Bouman, “Plug-and-play unplugged: Optimization free reconstruction using consensus equilibrium,” SIAM J. Imaging Sci. , vol. 11, no. 3, pp. 2001–2020, 2018
2018
Later among the works it cites.
L. Bottou, F. E. Curtis, and J. Nocedal, “Optimization methods for large-scale machine learning,” SIAM Rev. , vol. 60, no. 2, pp. 223–311, 2018
2018
Later among the works it cites.
A. Lucas, M. Iliadis, R. Molina, and A. K. Katsaggelos, “Using deep neural networks for inverse problems in imaging: Beyond analytical methods,” IEEE Signal Process. Mag. , vol. 35, no. 1, pp. 20–36, Jan. 2018
2018
Later among the works it cites.
A. Fletcher, S. Rangan, S. Sarkar, and P. Schniter, “Plug-in estimation in high-dimensional linear inverse problems: A rigorous analysis,” in Proc. Advances in Neural Information Processing Systems 32 , Montréal, Canada, Dec 3-8, 2018, pp. 7451–7460
2018
Later among the works it cites.
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Y.-L. Yu, “Better approximation and faster algorithm using the proximal average,” in Proc. Advances in Neural Information Processing Systems 26 , 2013
2013
Cited alongside, same era.
N. Parikh and S. Boyd, “Proximal algorithms,” Foundations and Trends in Optimization , vol. 1, no. 3, pp. 123–231, 2014
2014
Cited alongside, same era.
W. Zhong and J. Kwok, “Fast stochastic alternating direction method of multipliers,” in Proc. 31th Int. Conf. Machine Learning (ICML) , Bejing, China, Jun 22-24, 2014, pp. 46–54
2014
Cited alongside, same era.
J. Tan, Y. Ma, and D. Baron, “Compressive imaging via approximate message passing with image denoising,” IEEE Trans. Signal Process. , vol. 63, no. 8, pp. 2085–2092, Apr. 2015
2015
Cited alongside, same era.
L. Tian, Z. Liu, L. Yeh, M. Chen, J. Zhong, and L. Waller, “Computational illumination for high-speed in vitro fourier ptychographic microscopy,” Optica , vol. 2, no. 10, pp. 904–911, 2015
2015
Cited alongside, same era.
A. Borji and L. Itti, “Cat2000: A large scale fixation dataset for boosting saliency research,” Comput. Vis. Patt. Recong. (CVPR) 2015 Workshop on ”Future of Datasets” , 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
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 Trans. Comp. Imag. , vol. 2, no. 4, pp. 408–423, December 2016
2016
Cited alongside, same era.
V. Shah and C. Hegde, “Solving linear inverse problems using GAN priors: An algorithm with provable guarantees,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. , Calgary, AB, Canada, Apr. 2018, pp. 4609–4613
2018
Later among the works it cites.
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, “Spectral normalization for generative adversarial networks,” in International Conference on Learning Representations (ICLR) , 2018
2018
Later among the works it cites.
R. Ling, W. Tahir, H. Lin, H. Lee, and L. Tian, “High-throughput intensity diffraction tomography with a computational microscope,” Biomed. Opt. Express , vol. 9, no. 5, pp. 2130–2141, May 2018
2018
Later among the works it cites.
W. Dong, P. Wang, W. Yin, G. Shi, F. Wu, and X. Lu, “Denoising prior driven deep neural network for image restoration,” IEEE Trans. Patt. Anal. and Machine Intell. , vol. 41, no. 10, pp. 2305–2318, Oct. 2019
2019
Later among the works it cites.
K. Zhang, W. Zuo, and L. Zhang, “Deep plug-and-play super-resolution for arbitrary blur kernels,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR) , Long Beach, CA, USA, Jun. 2019, pp. 1671–1681
2019
Later among the works it cites.
Y. Sun, B. Wohlberg, and U. S. Kamilov, “An online plug-and-play algorithm for regularized image reconstruction,” IEEE Trans. Comput. Imaging , vol. 5, no. 3, pp. 395–408, Sep. 2019
2019
Later among the works it cites.
T. Tirer and R. Giryes, “Image restoration by iterative denoising and backward projections,” IEEE Trans. Image Process. , vol. 28, no. 3, pp. 1220–1234, 2019
2019
Later among the works it cites.
A. M. Teodoro, J. M. Bioucas-Dias, and M. Figueiredo, “A convergent image fusion algorithm using scene-adapted Gaussian-mixture-based denoising,” IEEE Trans. Image Process. , vol. 28, no. 1, pp. 451–463, Jan. 2019
2019
Later among the works it cites.
E. K. Ryu, J. Liu, S. Wang, X. Chen, Z. Wang, and W. Yin, “Plug-and-play methods provably converge with properly trained denoisers,” in Proc. 36th Int. Conf. Machine Learning (ICML) , Long Beach, CA, USA, Jun. 2019, pp. 5546–5557
2019
Later among the works it cites.
F. Huang, S. Chen, and H. Huang, “Faster stochastic alternating direction method of multipliers for nonconvex optimization,” in Proc. 36th Int. Conf. Machine Learning (ICML) , Long Beach, CA, USA, June 10-15, 2019, pp. 2839–2848
2019
Later among the works it cites.
Y. Sun, J. Liu, and U. S. Kamilov, “Block coordinate regularization by denoising,” in Advances in Neural Information Processing Systems 33 , Vancouver, BC, Canada, December 8-14, 2019, pp. 382–392
2019
Later among the works it cites.
G. Mataev, M. Elad, and P. Milanfar, “DeepRED: Deep image prior powered by RED,” in Proc. IEEE Int. Conf. Comp. Vis. Workshops (ICCVW) , Seoul, South Korea, Oct 27-Nov 2, 2019, pp. 1–10
2019
Later among the works it cites.
R. Hyder, V. Shah, C. Hegde, and M. S. Asif, “Alternating phase projected gradient descent with generative priors for solving compressive phase retrieval,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. , Brighton, UK, May 2019, pp. 7705–7709
2019
Later among the works it cites.
A. Raj, Y. Li, and Y. Bresler, “GAN-based projector for faster recovery in compressed sensing with convergence guarantees,” in Proc. IEEE Int. Conf. Comp. Vis. (ICCV) , Seoul, South Korea, Oct 27-Nov 2, 2019, pp. 5601–5610
2019
Later among the works it cites.
F. Latorre, A. Eftekhari, and V. Cevher, “Fast and provable ADMM for learning with generative priors,” in Advances in Neural Information Processing Systems 33 , Vancouver, BC, USA, December 8-14, 2019, pp. 12 027–12 039
2019
Later among the works it cites.
M. Fazlyab, A. Robey, H. H., M. Marari, and G. Pappas, “Efficient and accurate estimation of Lipschitz constants for deep neural networks,” in Proc. Advances in Neural Information Processing Systems 33 , Vancouver, BC, Canada, Dec. 2019, pp. 11 427–11 438
2019
Later among the works it cites.
H. Sedghi, V. Gupta, and P. M. Long, “The singular values of convolutional layers,” in International Conference on Learning Representations (ICLR) , 2019
2019
Later among the works it cites.
R. Ahmad, C. A. Bouman, G. T. Buzzard, S. Chan, S. Liu, E. T. Reehorst, and P. Schniter, “Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery,” IEEE Signal Processing Magazine , vol. 37, no. 1, pp. 105–116, 2020
2020
Closest in time.
F. Knoll, K. Hammernik, C. Zhang, S. Moeller, T. Pock, D. K. Sodickson, and M. Akcakaya, “Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues,” IEEE Signal Process. Mag. , vol. 37, no. 1, pp. 128–140, Jan. 2020
2020
Closest in time.
M. R. Kellman, E. Bostan, N. A. Repina, and L. Waller, “Physics-based learned design: Optimized coded-illumination for quantitative phase imaging,” IEEE Trans. Comput. Imag. , vol. 5, no. 3, pp. 344–353, 2020
2020
Closest in time.
M. Terris, A. Repetti, J.-C. Pesquet, and Y. Wiaux, “Building firmly nonexpansive convolutional neural networks,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. , Barcelona, Spain, May 2020, pp. 8658–8662
2020
Closest in time.