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A broad class of problems at the core of computational imaging, sensing, and low-level computer vision reduces to the inverse problem of extracting latent images that follow a prior distribution, from measurements taken under a known physical image formation model.
Nonlinear image recovery with half-quadratic regularization
D. Geman and C. Yang · 1995
Earlier work this paper cites.
The Scientist and Engineer’s Guide to Digital Signal Processing
S. Smith · 1997
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2001
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.
Convex Optimization
S. Boyd and L. Vandenberghe · 2004
Earlier work this paper cites.
Fields of experts: A framework for learning image priors
S. Roth and M. Black · 2005
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.
Image and depth from a conventional camera with a coded aperture
A. Levin, R. Fergus, F. Durand, and W. Freeman · 2007
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.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Fast image deconvolution using hyper-Laplacian priors
D. Krishnan and R. Fergus · 2009
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Understanding the difficulty of training deep feedforward neural networks
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Learning fast approximations of sparse coding
K. Gregor and Y. LeCun · 2010
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A first-order primal-dual algorithm for convex problems with applications to imaging
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From learning models of natural image patches to whole image restoration
D. Zoran and Y. Weiss · 2011
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BM3D frames and variational image deblurring
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Cited alongside, same era.
Shrinkage fields for effective image restoration
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Later among the works it cites.
Deep convolutional neural network for image deconvolution
L. Xu, J. Ren, C. Liu, and J. Jia · 2014
Later among the works it cites.
On learning optimized reaction diffusion processes for effective image restoration
Y. Chen, W. Yu, and T. Pock · 2015
Later among the works it cites.
Decoupled algorithm for MRI reconstruction using nonlocal block matching model: BM3D-MRI
E. Eksioglu · 2016
Later among the works it cites.
Techniques for gradient-based bilevel optimization with non-smooth lower level problems
p. Ochs, R. Ranftl, T. Brox, and T. Pock · 2016
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Proximal deep structured models
S. Wang, S. Fidler, and R. Urtasun · 2016
Later among the works it cites.
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A machine learning approach for non-blind image deconvolution
C. Schuler, H. Burger, S. Harmeling, and B. Scholkopf · 2013
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
Cited alongside, same era.
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator
X. Qu, Y. Hou, F. Lam, D. Guo, J. Zhong, and Z. Chen · 2014
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Accelerating magnetic resonance imaging via deep learning
S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang · 2016
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Deep ADMM-Net for compressive sensing MRI
Y. Yang, J. Sun, H. Li, and Z. Xu · 2016
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Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction
Z. Zhan, J.-F. Cai, D. Guo, Y. Liu, Z. Chen, and X. Qu · 2016
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