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Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency.
Nonlinear total variation based noise removal algorithms
L. Rudin, S. Osher, and E. Fatemi · 1992
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D. Geman and C. Yang · 1995
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C. Tomasi and R. Manduchi · 1998
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Anisotropic diffusion in image processing
J. Weickert · 1998
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A review of image denoising algorithms, with a new one
B. C. A. Buades and J. M. Morel · 2005
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Image denoising via sparse and redundant representations over learned dictionaries
M. Elad and M. Aharon · 2006
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Image denoising by sparse 3-D transform-domain collaborative filtering
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian · 2007
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Image and depth from a conventional camera with a coded aperture
A. Levin, R. Fergus, F. Durand, and W. T. Freeman · 2007
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An introduction to compressive sampling
E. J. Candès and M. B. Wakin · 2008
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Training an active random field for real-time image denoising
A. Barbu · 2009
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Fast image deconvolution using hyper-laplacian priors
D. Krishnan and R. Fergus · 2009
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Non-local sparse models for image restoration
J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman · 2009
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Fields of experts
S. Roth and M. Black · 2009
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Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
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A first-order primal-dual algorithm for convex problems with applications to imaging
A. Chambolle and T. Pock · 2011
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Blind deconvolution using a normalized sparsity measure
D. Krishnan, T. Tay, and R. Fergus · 2011
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Efficient marginal likelihood optimization in blind deconvolution
A. Levin, Y. Weiss, F. Durand, and W. T. Freeman · 2011
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A tour of modern image filtering: New insights and methods, both practical and theoretical
P. Milanfar · 2013
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Proximal algorithms
N. Parikh and S. Boyd · 2013
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Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
J. Ragan-Kelley, C. Barnes, A. Adams, S. Paris, F. Durand, and S. Amarasinghe · 2013
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Discriminative non-blind deblurring
U. Schmidt, C. Rother, S. Nowozin, J. Jancsary, and S. Roth · 2013
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Plug-and-play priors for model based reconstruction
S. V. Venkatakrishnan, C. A. Bouman, and B. Wohlberg · 2013
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Weighted nuclear norm minimization with application to image denoising
S. Gu, L. Zhang, W. Zuo, and X. Feng · 2014
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From learning models of natural image patches to whole image restoration
D. Zoran and Y. Weiss · 2011
Cited alongside, same era.
Image denoising: Can plain neural networks compete with BM3D?
H. C. Burger, C. J. Schuler, and S. Harmeling · 2012
Cited alongside, same era.
Regression tree fields - an efficient, non-parametric approach to image labeling problems
J. Jancsary, S. Nowozin, T. Sharp, and C. Rother · 2012
Cited alongside, same era.
Revisiting loss-specific training of filter-based mrfs for image restoration
Y. Chen, T. Pock, R. Ranftl, and H. Bischof · 2013
Cited alongside, same era.
Nonlocally centralized sparse representation for image restoration
W. Dong, L. Zhang, G. Shi, and X. Li · 2013
Cited alongside, same era.
Natural image denoising with convolutional networks
V. Jain and H. Seung
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Flexisp: a flexible camera image processing framework
F. Heide, M. Steinberger, Y.-T. Tsai, M. Rouf, D. Pajak, D. Reddy, O. Gallo, J. Liu, W. Heidrich, K. Egiazarian, et al · 2014
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Shrinkage fields for effective image restoration
U. Schmidt and S. Roth · 2014
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Global image denoising
H. Talebi and P. Milanfar · 2014
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On learning optimized reaction diffusion processes for effective image restoration
Y. Chen, W. Yu, and T. Pock · 2015
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The return of the gating network: Combining generative models and discriminative training in natural image priors
D. Rosenbaum and Y. Weiss · 2015
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