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Prior probability models are a fundamental component of many image processing problems, but density estimation is notoriously difficult for high-dimensional signals such as photographic images.
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J Portilla, V Strela, M J Wainwright, and E P Simoncelli · 2008
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Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Y. Hel-Or and D. Shaked · 2008
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Sampling theorems for signals from the union of finite-dimensional linear subspaces
T. Blumensath and M. E. Davies · 2009
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Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Yunjin Chen and Thomas Pock · 2017
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Deep energy estimator networks
Saeed Saremi, Bernhard Schölkopf, Arash Mehrjou, and Aapo Hyvärinen · 2018
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Ista-net: Interpretable optimization-inspired deep network for image compressive sensing
Jian Zhang and Bernard Ghanem · 2018
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End-to-end blind image quality assessment using deep neural networks
K. Ma, W. Liu, K. Zhang, Z. Duanmu, Z. Wang, and W. Zuo · 2018
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MNIST handwritten digit database
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Mnist handwritten digit database
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Pascal Vincent · 2011
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Least squares estimation without priors or supervision
M Raphan and E P Simoncelli · 2011
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Image denoising: Can plain neural networks compete with BM3D?
Harold C Burger, Christian J Schuler, and Stefan Harmeling · 2012
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Plug-and-play priors for model based reconstruction
Singanallur V Venkatakrishnan, Charles A Bouman, and Brendt Wohlberg · 2013
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Deepred: Deep image prior powered by red
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Image restoration and reconstruction using targeted plug-and-play priors
Afonso M Teodoro, José M Bioucas-Dias, and Mário AT Figueiredo · 2019
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An online plug-and-play algorithm for regularized image reconstruction
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Regularization by denoising: Clarifications and new interpretations
E. T. Reehorst and P. Schniter · 2019
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Learning energy-based models in high-dimensional spaces with multi-scale denoising score matching, 2019
Zengyi Li, Yubei Chen, and Friedrich T. Sommer · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Neural empirical Bayes
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Annealed denoising score matching: Learning energy-based models in high-dimensional spaces
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Deep image prior
D Ulyanov, A Vedaldi, and V Lempitsky · 2020
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Learning generative models using denoising density estimators
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Robust and interpretable blind image denoising via bias-free convolutional neural networks
S Mohan*, Z Kadkhodaie*, E P Simoncelli, and C Fernandez-Granda · 2020
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Self-supervised bayesian deep learning for image recovery with applications to compressive sensing
Tongyao Pang, Yuhui Quan, and Hui Ji · 2020
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