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Plug-and-play (PnP) is a non-convex framework that combines ADMM or other proximal algorithms with advanced denoiser priors.
Generalized cross-validation as a method for choosing a good ridge parameter
Golub, G. H., Heath, M., and Wahba, G · 1979
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Phase retrieval algorithms: a comparison
Fienup, J. R · 1982
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Lin, L · 1992
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The use of the l-curve in the regularization of discrete ill-posed problems
Hansen, P. C. and O’Leary, D. P · 1993
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Nonlinear image recovery with half-quadratic regularization
Geman, D · 1995
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Policy gradient methods for reinforcement learning with function approximation
Sutton, R., Mcallester, D., Singh, S., and Mansour, Y · 2000
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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Martin, D., Fowlkes, C., Tal, D., and Malik, J · 2001
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Segmentation-free statistical image reconstruction for polyenergetic x-ray computed tomography
Elbakri, I. A. and Fessler, J. A · 2002
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A non-local algorithm for image denoising
Buades, A., Coll, B., and Morel, J.-M · 2005
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An iterative regularization method for total variation-based image restoration
Osher, S., Burger, M., Goldfarb, D., Xu, J., and Yin, W · 2005
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Policy gradient methods for robotics
Peters, J. and Schaal, S · 2006
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Image denoising by sparse 3-d transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K · 2007
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Model-based 2.5-d deconvolution for extended depth of field in brightfield microscopy
Aguet, F., Van De Ville, D., and Unser, M · 2008
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Generalized sure for exponential families: Applications to regularization
Eldar, Y. C · 2008
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Sparse representations for limited data tomography
Liao, H. Y. and Sapiro, G · 2008
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An efficient algorithm for compressed mr imaging using total variation and wavelets
Ma, S., Yin, W., Zhang, Y., and Chakraborty, A · 2008
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Beck, A. and Teboulle, M · 2009
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Non-local sparse models for image restoration
Mairal, J., Bach, F. R., Ponce, J., Sapiro, G., and Zisserman, A · 2009
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Image deblurring by augmented lagrangian with bm3d frame prior
Danielyan, A., Katkovnik, V., and Egiazarian, K · 2010
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A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science
Esser, E., Zhang, X., and Chan, T. F · 2010
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Model-based image reconstruction for mri
Fessler, J. A · 2010
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Learning fast approximations of sparse coding
Gregor, K. and LeCun, Y · 2010
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Efficient mr image reconstruction for compressed mr imaging
Huang, J., Zhang, S., and Metaxas, D · 2010
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Mr image reconstruction from highly undersampled k-space data by dictionary learning
Ravishankar, S. and Bresler, Y · 2010
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A fast alternating direction method for tvl1-l2 signal reconstruction from partial fourier data
Yang, J., Zhang, Y., and Yin, W · 2010
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et al · 2011
Earlier work this paper cites.
A first-order primal-dual algorithm for convex problems with applications to imaging
Chambolle, A. and Pock, T · 2011
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The projected gsure for automatic parameter tuning in iterative shrinkage methods
Giryes, R., Elad, M., and Eldar, Y. C · 2011
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From learning models of natural image patches to whole image restoration
Zoran, D. and Weiss, Y · 2011
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Regularization parameter selection for nonlinear iterative image restoration and mri reconstruction using gcv and sure-based methods
Ramani, S., Liu, Z., Rosen, J., Nielsen, J.-F., and Fessler, J. A · 2012
Cited alongside, same era.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Cited alongside, same era.
Plug-and-play priors for model based reconstruction
Venkatakrishnan, S. V., Bouman, C. A., and Wohlberg, B · 2013
Cited alongside, same era.
Wide-field, high-resolution fourier ptychographic microscopy
Zheng, G., Horstmeyer, R., and Yang, C · 2013
Cited alongside, same era.
Phase retrieval via wirtinger flow: Theory and algorithms
Candes, E., Li, X., and Soltanolkotabi, M · 2014
Cited alongside, same era.
Plug-and-play admm for image restoration: Fixed-point convergence and applications
Chan, S. H., Wang, X., and Elgendy, O. A · 2017
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Unrolled optimization with deep priors
Diamond, S., Sitzmann, V., Heide, F., and Wetzstein, G · 2017
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Weighted nuclear norm minimization and its applications to low level vision
Gu, S., Xie, Q., Meng, D., Zuo, W., Feng, X., and Zhang, L · 2017
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A plug-and-play priors approach for solving nonlinear imaging inverse problems
Kamilov, U. S., Mansour, H., and Wohlberg, B · 2017
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Phase retrieval from noisy data based on sparse approximation of object phase and amplitude
Katkovnik, V · 2017
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The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S., Van Gool, L., Williams, C., Winn, J., and Zisserman, A · 2014
Cited alongside, same era.
Flexisp: A flexible camera image processing framework
Heide, F., Steinberger, M., Tsai, Y.-T., Rouf, M., Pajak, D., Reddy, D., Gallo, O., Liu, J., Heidrich, W., Egiazarian, K., et al · 2014
Cited alongside, same era.
Deep unfolding: Model-based inspiration of novel deep architectures
Hershey, J. R., Roux, J. L., and Weninger, F · 2014
Cited alongside, same era.
Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations
Katz, O., Heidmann, P., Fink, M., and Gigan, S · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Proximal algorithms
Parikh, N., Boyd, S., et al · 2014
Cited alongside, same era.
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator
Qu, X., Hou, Y., Lam, F., Guo, D., Zhong, J., and Chen, Z · 2014
Cited alongside, same era.
Learning proximal operators: Using denoising networks for regularizing inverse imaging problems
Meinhardt, T., Moller, M., Hazirbas, C., and Cremers, D · 2017
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Primal-dual plug-and-play image restoration
Ono, S · 2017
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One network to solve them all – solving linear inverse problems using deep projection models
Rick Chang, J. H., Li, C.-L., Poczos, B., Vijaya Kumar, B. V. K., and Sankaranarayanan, A. C · 2017
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The little engine that could: Regularization by denoising (red)
Romano, Y., Elad, M., and Milanfar, P · 2017
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Multi-resolution data fusion for super-resolution electron microscopy
Sreehari, S., Venkatakrishnan, S., Bouman, K. L., Simmons, J. P., Drummy, L. F., and Bouman, C. A · 2017
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Memnet: A persistent memory network for image restoration
Tai, Y., Yang, J., Liu, X., and Xu, C · 2017
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Parameter-free plug-and-play admm for image restoration
Wang, X. and Chan, S. H · 2017
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Learned primal-dual reconstruction
Adler, J. and Oktem, O · 2018
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Denoising prior driven deep neural network for image restoration
Dong, W., Wang, P., Yin, W., Shi, G., Wu, F., and Lu, X · 2018
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Optimizing a parameterized plug-and-play admm for iterative low-dose ct reconstruction
He, J., Yang, Y., Wang, Y., Zeng, D., Bian, Z., Zhang, H., Sun, J., Xu, Z., and Ma, J · 2018
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prdeep: Robust phase retrieval with a flexible deep network
Metzler, C., Schniter, P., Veeraraghavan, A., et al · 2018
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A convergent image fusion algorithm using scene-adapted gaussian-mixture-based denoising
Teodoro, A. M., Bioucas-Dias, J. M., and Figueiredo, M. A · 2018
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Image restoration by iterative denoising and backward projections
Tirer, T. and Giryes, R · 2018
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Crafting a toolchain for image restoration by deep reinforcement learning
Yu, K., Dong, C., Lin, L., and Change Loy, C · 2018
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Ista-net: Interpretable optimization-inspired deep network for image compressive sensing
Zhang, J. and Ghanem, B · 2018
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Ffdnet: Toward a fast and flexible solution for cnn-based image denoising
Zhang, K., Zuo, W., and Zhang, L · 2018
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Performance analysis of plug-and-play admm: A graph signal processing perspective
Chan, S. H · 2019
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Momentum-net: Fast and convergent iterative neural network for inverse problems
Chun, I. Y., Huang, Z., Lim, H., and Fessler, J. A · 2019
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Fully convolutional network with multi-step reinforcement learning for image processing
Furuta, R., Inoue, N., and Yamasaki, T · 2019
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Learning to paint with model-based deep reinforcement learning
Huang, Z., Heng, W., and Zhou, S · 2019
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Plug-and-play methods provably converge with properly trained denoisers
Ryu, E., Liu, J., Wang, S., Chen, X., Wang, Z., and Yin, W · 2019
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Differentiable linearized admm
Xie, X., Wu, J., Liu, G., Zhong, Z., and Lin, Z · 2019
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Path-restore: Learning network path selection for image restoration
Yu, K., Wang, X., Dong, C., Tang, X., and Loy, C. C · 2019
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