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Image denoisers have been shown to be powerful priors for solving inverse problems in imaging.
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Plug-and-play priors for bright field electron tomography and sparse interpolation
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First-Order Methods in Optimization
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Deep mean-shift priors for image restoration
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Compressed sensing using generative priors
A. Bora, A. Jalal, E. Price, and A. G. Dimakis · 2017
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Plug-and-play ADMM for image restoration: Fixed-point convergence and applications
S. H. Chan, X. Wang, and O. A. Elgendy · 2017
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Low-dose CT with a residual encoder-decoder convolutional neural network
H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang · 2017
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Deep convolutional neural network for inverse problems in imaging
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser · 2017
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A deep convolutional neural network using directional wavelets for low-dose x-ray CT reconstruction
E. Kang, J. Min, and J. C. Ye · 2017
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Enhanced deep residual networks for single image super-resolution
B. Lim, S. Son, H. Kim, S. Nah, and K. Mu L · 2017
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Convolutional neural networks for inverse problems in imaging: A review
M. T. McCann, K. H. Jin, and M. Unser · 2017
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Learning proximal operators: Using denoising networks for regularizing inverse imaging problems
T. Meinhardt, M. Moeller, C. Hazirbas, and D. Cremers · 2017
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The little engine that could: Regularization by denoising (RED)
Y. Romano, M. Elad, and P. Milanfar · 2017
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Modl: Model-based deep learning architecture for inverse problems
H. K. Aggarwal, M. P. Mani, and M. Jacob · 2018
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Plug-and-play unplugged: Optimization free reconstruction using consensus equilibrium
G. T. Buzzard, S. H. Chan, S. Sreehari, and C. A. Bouman · 2018
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Model-based learning for accelerated, limited-view 3-d photoacoustic tomography
A. Hauptmann, F. Lucka, M. Betcke, N. Huynh, J. Adler, B. Cox, P. Beard, S. Ourselin, and S. Arridge · 2018
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Using deep neural networks for inverse problems in imaging: Beyond analytical methods
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
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Provable convergence of plug-and-play priors with mmse denoisers
X. Xu, Y. Sun, J. Liu, B. Wohlberg, and U. S. Kamilov · 2020
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Projected distribution loss for image enhancement
M. Delbracio, H. Talebei, and P. Milanfar · 2021
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Deep equilibrium architectures for inverse problems in imaging
D. Gilton, G. Ongie, and R. Willett · 2021
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On Bayesian estimation and proximity operators
R. Gribonval and M. Nikolova · 2021
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Stochastic solutions for linear inverse problems using the prior implicit in a denoiser
Z. Kadkhodaie and E. P. Simoncelli · 2021
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A. Lucas, M. Iliadis, R. Molina, and A. K. Katsaggelos · 2018
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prDeep: Robust phase retrieval with a flexible deep network
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ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing
J. Zhang and B. Ghanem · 2018
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Denoising prior driven deep neural network for image restoration
W. Dong, P. Wang, W. Yin, G. Shi, F. Wu, and X. Lu · 2019
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Regularization by denoising: Clarifications and new interpretations
E. T. Reehorst and P. Schniter · 2019
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Plug-and-play methods provably converge with properly trained denoisers
E. K. Ryu, J. Liu, S. Wang, X. Chen, Z. Wang, and W. Yin · 2019
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An online plug-and-play algorithm for regularized image reconstruction
Y. Sun, B. Wohlberg, and U. S. Kamilov · 2019
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Swinir: Image restoration using swin transformer
J. Liang, J. Cao, G. Sun, K. Zhang, L. Van G., and R. Timofte · 2021
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Recovery analysis for plug-and-play priors using the restricted eigenvalue condition
J. Liu, S. Asif, B. Wohlberg, and U. S. Kamilov · 2021
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Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing
V. Monga, Y. Li, and Y. C. Eldar · 2021
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Scalable plug-and-play ADMM with convergence guarantees
Y. Sun, Z. Wu, B. Wohlberg, and U. S. Kamilov · 2021
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Bayesian imaging using plug & play priors: When Langevin meets Tweedie
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Online deep equilibrium learning for regularization by denoising
J. Liu, X. Xu, W. Gan, S. Shoushtari, and U. S. Kamilov · 2022
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Zero-shot image restoration using denoising diffusion null-space model
Y. Wang, J. Yu, and J. Zhang · 2022
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Plug-and-play image restoration with deep denoiser prior
K. Zhang, Y. Li, W. Zuo, L. Zhang, L. Van Gool, and R. Timofte · 2022
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Diffusion posterior sampling for general noisy inverse problems
H. Chung, J. Kim, M. T. Mccann, M. L. K., and J. C. Ye · 2023
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Inversion by direct iteration: An alternative to denoising diffusion for image restoration
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Block coordinate plug-and-play methodsfor blind inverse problems
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Plug-and-play methods for integrating physical and learned models in computational imaging
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Denoising diffusion models for plug-and-play image restoration
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