Fetching the paper…
Reading the bibliography…
Plug-and-play priors (PnP) is an image reconstruction framework that uses an image denoiser as an imaging prior.
1907
Earlier work this paper cites.
L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D , vol. 60, no. 1–4, pp. 259–268, Nov. 1992
1992
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.
M. A. T. Figueiredo and R. D. Nowak, “Wavelet-based image estimation: An empirical bayes approach using Jeffreys’ noninformative prior,” IEEE Trans. Image Process. , vol. 10, no. 9, pp. 1322–1331, Sep. 2001
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, Aug. 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, Nov. 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.
M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. , vol. 15, no. 12, pp. 3736–3745, Dec. 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, Aug. 2007
2007
Earlier work this paper cites.
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space,” in Proc. IEEE Int. Conf. Image Proc. (ICIP 2017) , San Antonio, TX, USA, 2007
2007
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, Nov. 2009
2009
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.
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, Sep. 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, Aug. 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.
A. Danielyan, V. Katkovnik, and K. Egiazarian, “BM3D frames and variational image deblurring,” IEEE Trans. Image Process. , vol. 21, no. 4, pp. 1715–1728, April 2012
2012
Earlier work this paper cites.
Z. Yang and M. Jacob, “Nonlocal regularization of inverse problems: A unified variational framework,” IEEE Trans. Image Process. , vol. 22, no. 8, pp. 3192–3203, Aug. 2013
2013
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) , Austin, TX, USA, December 3-5, 2013, pp. 945–948
2013
Earlier work this paper cites.
A. Matakos, S. Ramani, and J. Fessler, “Accelerated edge-preserving image restoration without boundary artifacts,” IEEE Trans. Image Process. , vol. 22, no. 5, pp. 2019–2029, May 2013
2013
Cited alongside, same era.
M. Almeida and M. Figueiredo, “Deconvolving images with unknown boundaries using the alternating direction method of multipliers,” IEEE Trans. Ima , vol. 22, no. 8, pp. 3074–3086, Aug. 2013
2013
Cited alongside, same era.
U. S. Kamilov, P. Pad, A. Amini, and M. Unser, “MMSE estimation of sparse Lévy processes,” IEEE Trans. Signal Process. , vol. 61, no. 1, pp. 137–147, Jan. 2013
2013
Cited alongside, same era.
A. Kazerouni, U. S. Kamilov, E. Bostan, and M. Unser, “Bayesian denoising: From MAP to MMSE using consistent cycle spinning,” IEEE Signal Process. Lett. , vol. 20, no. 3, pp. 249–252, Mar. 2013
2013
Cited alongside, same era.
T. Meinhardt, M. Moeller, C. Hazirbas, and D. Cremers, “Learning proximal operators: Using denoising networks for regularizing inverse imaging problems,” in Proc. IEEE Int. Conf. Comp. Vis. (ICCV) , Venice, Italy, October 22-29, 2017, pp. 1799–1808
2017
Later among the works it cites.
Y. Romano, M. Elad, and P. Milanfar, “The little engine that could: Regularization by denoising (RED),” SIAM J. Imaging Sci. , vol. 10, no. 4, pp. 1804–1844, 2017
2017
Later among the works it cites.
S. A. Bigdeli, M. Jin, P. Favaro, and M. Zwicker, “Deep mean-shift priors for image restoration,” in Proc. Advances in Neural Information Processing Systems 31 , Long Beach, CA, USA, Dec. 2017
2017
Later among the works it cites.
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, Jul. 2017
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. Gribonval and P. Machart, “Reconciling “priors” & “priors” without prejudice?” in Proc. Advances in Neural Information Processing Systems 26 , Lake Tahoe, NV, USA, Dec. 2013, pp. 2193–2201
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.
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.
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, Dec. 2016
2016
Cited alongside, same era.
C. A. Metzler, A. Maleki, and R. G. Baraniuk, “From denoising to compressed sensing,” IEEE Trans. Inf. Theory , vol. 62, no. 9, pp. 5117–5144, Sep. 2016
2016
Cited alongside, same era.
C. A. Metzler, A. Maleki, and R. Baraniuk, “BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising,” in Proc. IEEE Int. Conf. Image Proc. , Phoenix, AZ, USA, September 25-28, 2016, pp. 2504–2508
2016
Cited alongside, same era.
B. Wohlberg, “Efficient algorithms for convolutional sparse representations,” IEEE Trans. Image Process. , vol. 25, no. 1, pp. 301–315, Jan. 2016
2016
Cited alongside, same era.
U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Trans. Comp. Imag. , vol. 2, no. 1, pp. 59–70, Mar. 2016
2016
Cited alongside, same era.
Y. Chen and T. Pock, “Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 6, pp. 1256–1272, Jun. 2017
2017
Later among the works it cites.
A. Beck, First-Order Methods in Optimization . SIAM, 2017
2017
Later among the works it cites.
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.
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, 2018
2018
Later among the works it cites.
C. A. Metzler, P. Schniter, A. Veeraraghavan, and R. G. Baraniuk, “prDeep: Robust phase retrieval with a flexible deep network,” in Proc. 35th Int. Conf. Machine Learning (ICML) , Stockholm, Sweden, Jun. 2018
2018
Later among the works it cites.
2018
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.
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.
E. K. Ryu, J. Liu, S. Wnag, 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
2019
Later among the works it cites.
E. T. Reehorst and P. Schniter, “Regularization by denoising: Clarifications and new interpretations,” IEEE Trans. Comput. Imag. , vol. 5, no. 1, pp. 52–67, Mar. 2019
2019
Later among the works it cites.
G. Mataev, P. Milanfar, and M. Elad, “DeepRED: Deep image prior powered by RED,” in Proc. IEEE Int. Conf. Comp. Vis. Workshops (ICCVW) , Oct. 2019
2019
Later among the works it cites.
Y. Sun, J. Liu, and U. S. Kamilov, “Block coordinate regularization by denoising,” in Proc. Advances in Neural Information Processing Systems 33 , Vancouver, BC, Canada, Dec. 2019
2019
Later among the works it cites.
J. Liu, Y. Sun, X. Xu, and U. S. Kamilov, “Image restoration using total variation regularized deep image prior,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process. , Brighton, UK, May 2019, pp. 7715–7719
2019
Later among the works it cites.