Fetching the paper…
Reading the bibliography…
The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors.
Convex Analysis
R. T. Rockafellar, · 1970
Earlier work this paper cites.
“Nonlinear total variation based noise removal algorithms,”
L. I. Rudin, S. Osher, and E. Fatemi, · 1992
Earlier work this paper cites.
“A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,”
D. Martin, C. Fowlkes, D. Tal, and J. Malik, · 2001
Earlier work this paper cites.
“An EM algorithm for wavelet-based image restoration,”
M. A. T. Figueiredo and R. D. Nowak, · 2003
Earlier work this paper cites.
“An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,”
I. Daubechies, M. Defrise, and C. De Mol, · 2004
Earlier work this paper cites.
“A ℓ 1 \ell_{1} -unified variational framework for image restoration,”
J. Bect, L. Blanc-Feraud, G. Aubert, and A. Chambolle, · 2004
Earlier work this paper cites.
Introductory Lectures on Convex Optimization: A Basic Course
Y. Nesterov, · 2004
Earlier work this paper cites.
Convex Optimization
S. Boyd and L. Vandenberghe, · 2004
Earlier work this paper cites.
“Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,”
E. J. Candès, J. Romberg, and T. Tao, · 2006
Earlier work this paper cites.
“Compressed sensing,”
D. L. Donoho, · 2006
Earlier work this paper cites.
“A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration,”
J. M. Bioucas-Dias and M. A. T. Figueiredo, · 2007
Earlier work this paper cites.
“Image denoising by sparse 3-D transform-domain collaborative filtering,”
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, · 2007
Earlier work this paper cites.
“Sparse MRI: The application of compressed sensing for rapid MR imaging,”
M. Lustig, D. L. Donoho, and J. M. Pauly, · 2007
Earlier work this paper cites.
“An introduction to compressive sensing,”
E. Candès and M. B. Wakin, · 2008
Earlier work this paper cites.
“Compressed sensing MRI,”
M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, · 2008
Earlier work this paper cites.
“Simultaneous analysis of lasso and Dantzig selector,”
P. T. Bickel, R. Ya’acov, and T. B. Alexandre, · 2009
Earlier work this paper cites.
“Fast gradient-based algorithm for constrained total variation image denoising and deblurring problems,”
A. Beck and M. Teboulle, · 2009
Earlier work this paper cites.
“A fast iterative shrinkage-thresholding algorithm for linear inverse problems,”
A. Beck and M. Teboulle, · 2009
Earlier work this paper cites.
“Learning fast approximation of sparse coding,”
K. Gregor and Y. LeCun, · 2010
Earlier work this paper cites.
Sparse and Redundant Representations
M. Elad, · 2010
Earlier work this paper cites.
“Plug-and-play priors for model based reconstruction,”
S. V. Venkatakrishnan, C. A. Bouman, and B. Wohlberg, · 2013
Earlier work this paper cites.
A Methematical Introduction to Compressive Sensing
S. Foucart and H. Rauhut, · 2013
Earlier work this paper cites.
“Proximal algorithms,”
N. Parikh and S. Boyd, · 2014
Earlier work this paper cites.
“Shrinkage fields for effective image restoration,”
U. Schmidt and S. Roth, · 2014
Earlier work this paper cites.
“Compressive imaging via approximate message passing with image denoising,”
J. Tan, Y. Ma, and D. Baron, · 2015
Earlier work this paper cites.
“On learning optimized reaction diffuction processes for effective image restoration,”
Y. Chen, W. Yu, and T. Pock, · 2015
Earlier work this paper cites.
“A deep learning approach to structured signal recovery,”
A. Mousavi, A. B. Patel, and R. G. Baraniuk, · 2015
Earlier work this paper cites.
“Plug-and-play priors for bright field electron tomography and sparse interpolation,”
S. Sreehari, S. V. Venkatakrishnan, B. Wohlberg, G. T. Buzzard, L. F. Drummy, J. P. Simmons, and C. A. Bouman, · 2016
Earlier work this paper cites.
“From denoising to compressed sensing,”
C. A. Metzler, A. Maleki, and R. G. Baraniuk, · 2016
Earlier work this paper cites.
“BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising,”
C. A. Metzler, A. Maleki, and R. Baraniuk, · 2016
Earlier work this paper cites.
“Deep ADMM-Net for compressive sensing MRI,”
Y. Yang, J. Sun, H. Li, and Z. Xu, · 2016
Cited alongside, same era.
“ReconNet: Non-iterative reconstruction of images from compressively sensed measurements,”
K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok, · 2016
Cited alongside, same era.
“A primer on monotone operator methods,”
E. K. Ryu and S. Boyd, · 2016
Cited alongside, same era.
“Compressed sensing using generative priors,”
A. Bora, A. Jalal, E. Price, and A. G. Dimakis, · 2017
Cited alongside, same era.
“The little engine that could: Regularization by denoising (RED),”
Y. Romano, M. Elad, and P. Milanfar, · 2017
Cited alongside, same era.
“Learning deep CNN denoiser prior for image restoration,”
K. Zhang, W. Zuo, S. Gu, and L. Zhang, · 2017
Cited alongside, same era.
“Alternating phase projected gradient descent with generative priors for solving compressive phase retrieval,”
R. Hyder, V. Shah, C. Hegde, and M. S. Asif, · 2019
Later among the works it cites.
“Denoising prior driven deep neural network for image restoration,”
W. Dong, P. Wang, W. Yin, G. Shi, F. Wu, and X. Lu, · 2019
Later among the works it cites.
“Deep plug-and-play super-resolution for arbitrary blur kernels,”
K. Zhang, W. Zuo, and L. Zhang, · 2019
Later among the works it cites.
“Regularization by denoising: Clarifications and new interpretations,”
E. T. Reehorst and P. Schniter, · 2019
Later among the works it cites.
“Plug-and-play methods provably converge with properly trained denoisers,”
E. K. Ryu, J. Liu, S. Wnag, X. Chen, Z. Wang, and W. Yin, · 2019
Later among the works it cites.
“An online plug-and-play algorithm for regularized image reconstruction,”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Plug-and-play ADMM for image restoration: Fixed-point convergence and applications,”
S. H. Chan, X. Wang, and O. A. Elgendy, · 2017
Cited alongside, same era.
“Learning proximal operators: Using denoising networks for regularizing inverse imaging problems,”
T. Meinhardt, M. Moeller, C. Hazirbas, and D. Cremers, · 2017
Cited alongside, same era.
“Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,”
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, · 2017
Cited alongside, same era.
“A plug-and-play priors approach for solving nonlinear imaging inverse problems,”
U. S. Kamilov, H. Mansour, and B. Wohlberg, · 2017
Cited alongside, same era.
“Convolutional neural networks for inverse problems in imaging: A review,”
M. T. McCann, K. H. Jin, and M. Unser, · 2017
Cited alongside, same era.
Convex Analysis and Monotone Operator Theory in Hilbert Spaces
H. H. Bauschke and P. L. Combettes, · 2017
Cited alongside, same era.
Y. Sun, B. Wohlberg, and U. S. Kamilov, · 2019
Later among the works it cites.
“Image restoration by iterative denoising and backward projections,”
T. Tirer and R. Giryes, · 2019
Later among the works it cites.
“A convergent image fusion algorithm using scene-adapted Gaussian-mixture-based denoising,”
A. M. Teodoro, J. M. Bioucas-Dias, and M. A. T. Figueiredo, · 2019
Later among the works it cites.
“Block coordinate regularization by denoising,”
Y. Sun, J. Liu, and U. S. Kamilov, · 2019
Later among the works it cites.
“A style-based generator architecture for generative adversarial networks,”
T. Karras, S. Laine, and T. Aila, · 2019
Later among the works it cites.
“DeepRED: Deep image prior powered by RED,”
G. Mataev, M. Elad, and P. Milanfar, · 2019
Later among the works it cites.
“MoDL: Model-based deep learning architecture for inverse problems,”
H. K. Aggarwal, M. P. Mani, and M. Jacob, · 2019
Later among the works it cites.
“Efficient and accurate estimation of Lipschitz constants for deep neural networks,”
M. Fazlyab, A. Robey, Hassani. H., M. Marari, and G. Pappas, · 2019
Later among the works it cites.
“Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery,”
R. Ahmad, C. A. Bouman, G. T. Buzzard, S. Chan, S. Liu, E. T. Reehorst, and P. Schniter, · 2020
Later among the works it cites.
“Tuning-free plug-and-play proximal algorithm for inverse imaging problems,”
K. Wei, A. Aviles-Rivero, J. Liang, Y. Fu, C.-B. Schönlieb, and H. Huang, · 2020
Later among the works it cites.
“Provable convergence of plug-and-play priors with MMSE denoisers,”
X. Xu, Y. Sun, J. Liu, B. Wohlberg, and U. S. Kamilov, · 2020
Later among the works it cites.
“Analyzing and improving the image quality of StyleGAN,”
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, · 2020
Later among the works it cites.
“PULSE: Self-supervised photo upsampling via latent space exploration of generative models,”
S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, · 2020
Later among the works it cites.
“RARE: Image reconstruction using deep priors learned without ground truth,”
J. Liu, Y. Sun, C. Eldeniz, W. Gan, H. An, and U. S. Kamilov, · 2020
Later among the works it cites.
“Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues,”
F. Knoll, K. Hammernik, C. Zhang, S. Moeller, T. Pock, D. K. Sodickson, and M. Akcakaya, · 2020
Later among the works it cites.
“Deep learning techniques for inverse problems in imaging,”
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, · 2020
Later among the works it cites.
“Building firmly nonexpansive convolutional neural networks,”
M. Terris, A. Repetti, J.-C. Pesquet, and Y. Wiaux, · 2020
Later among the works it cites.
“Boosting the performance of plug-and-play priors via denoiser scaling,”
X. Xu, J. Liu, Y. Sun, B. Wohlberg, and U.S. Kamilov, · 2020
Later among the works it cites.
“Scalable plug-and-play ADMM with convergence guarantees,”
Y. Sun, Z. Wu, B. Wohlberg, and U. S. Kamilov, · 2021
Closest in time.
“Regularization by denoising via fixed-point projection (red-pro),”
R. Cohen, M. Elad, and P. Milanfar, · 2021
Closest in time.
“Intermediate layer optimizationfor inverse problems using deep generative models,”
G. Daras, J. Dean, A. Jalal, and A. G. Dimakis, · 2021
Closest in time.
“Joint reconstruction and calibration using regularization by denoising with application to computed tomography,”
M. Xie, J. Liu, Y. Sun, W. Gan, B. Wohlberg, and U. S. Kamilov, · 2021
Closest in time.
“SGD-Net: Efficient model-based feep learning with theoretical guarantees,”
J. Liu, Y. Sun, W. Gan, B. Wohlberg, and U. S. Kamilov, · 2021
Closest in time.
“Deep equilibrium architectures for inverse problems in imaging,”
D. Gilton, G. Ongie, and R. Willett, · 2021
Closest in time.