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Image restoration remains a challenging task in image processing.
“Tikhonov regularization of incorrectly posed problems,”
A. Tikhonov, · 1963
Earlier work this paper cites.
“Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,”
S. Geman and D. Geman, · 1987
Earlier work this paper cites.
“Optimal approximations by piecewise smooth functions and associated variational problems,”
D. Mumford and J. Shah, · 1989
Earlier work this paper cites.
“Nonlinear total variation based noise removal algorithms,”
L. Rudin, S. Osher, and E. Fatemi, · 1992
Earlier work this paper cites.
“Total variation based image restoration with free local constraints,”
L. I Rudin and S. Osher, · 1994
Earlier work this paper cites.
“Deterministic edge-preserving regularization in computed imaging,”
P. Charbonnier, L. Blanc-Féraud, G. Aubert, and M. Barlaud, · 1997
Earlier work this paper cites.
“Image recovery via total variation minimization and related problems,”
A. Chambolle and P.-L. Lions, · 1997
Earlier work this paper cites.
“Gradient-based learning applied to document recognition,”
Y LeCun, L Botto, Y Bengio, and P Haffner, · 1998
Earlier work this paper cites.
“An algorithm for total variation minimization and applications,”
A. Chambolle, · 2004
Earlier work this paper cites.
“An iterative regularization method for total variation-based image restoration,”
S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, · 2005
Earlier work this paper cites.
“Signal recovery by proximal forward-backward splitting,”
P. L. Combettes and V. R. Wajs, · 2005
Earlier work this paper cites.
“Analysis versus synthesis in signal priors,”
M. Elad, P. Milanfar, and R. Ron, · 2007
Earlier work this paper cites.
“A Douglas-Rachford splitting approach to nonsmooth convex variational signal recovery,”
P. L. Combettes and J.-C. Pesquet, · 2007
Earlier work this paper cites.
“Non-local regularization of inverse problems,”
G. Peyré, S. Bougleux, and L. Cohen, · 2008
Earlier work this paper cites.
“Monte-carlo SURE: A black-box optimization of regularization parameters for general denoising algorithms,”
S. Ramani, T. Blu, and M. Unser, · 2008
Earlier work this paper cites.
“Generalized SURE for exponential families: Applications to regularization,”
Y. C Eldar, · 2008
Earlier work this paper cites.
“Solving inverse problems with over-complete transforms and convex optimization techniques,”
L. Chaâri, N. Pustelnik, C. Chaux, and J.-C. Pesquet, · 2009
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.
Split Bregman algorithm, Douglas-Rachford splitting and frame shrinkage
S. Setzer, · 2009
Earlier work this paper cites.
“A SURE approach for digital signal/image deconvolution problems,”
J.-C. Pesquet, A. Benazza-Benyahia, and C. Chaux, · 2009
Earlier work this paper cites.
“Fields of experts,”
S. Roth and M. J. Black, · 2009
Earlier work this paper cites.
“Learning fast approximations of sparse coding,”
K. Gregor and Y. LeCun, · 2010
Cited alongside, same era.
“Proximal splitting methods in signal processing,”
P. L. Combettes and J.-C. Pesquet, · 2011
Cited alongside, same era.
“From learning models of natural image patches to whole image restoration,”
D. Zoran and Y. Weiss, · 2011
Cited alongside, same era.
“BM3D frames and variational image deblurring,”
A. Danielyan, V. Katkovnik, and K. Egiazarian, · 2012
Cited alongside, same era.
“On single image scale-up using sparse-representations,”
R. Reyde, M. Elad, and M. Protter, · 2012
Cited alongside, same era.
“A primal-dual splitting method for convex optimization involving lipschitzian, proximable and linear composite terms,”
L. Condat, · 2013
Cited alongside, same era.
“Deep Convolutional Neural Network for inverse problems in imaging,”
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, · 2017
Later among the works it cites.
“Learning deep CNN denoiser prior for image restoration,”
K. Zhang, W. Zuo, S. Gu, and L. Zhang, · 2017
Later among the works it cites.
“Learning deep cnn denoiser prior for image restoration,”
K. Zhang, W. Zuo, S. Gu, and L. Zhang, · 2017
Later among the works it cites.
“Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration,”
Y. Chen and T. Pock, · 2017
Later among the works it cites.
“Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,”
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, · 2017
Later among the works it cites.
“Using deep neural networks for inverse problems in imaging: Beyond analytical methods,”
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“A splitting algorithm for dual monotone inclusions involving cocoercive operators,”
B. C. Vũ, · 2013
Cited alongside, same era.
“Stein unbiased gradient estimator of the risk (sugar) for multiple parameter selection,”
C.-A. Deledalle, S. Vaiter, J. Fadili, and G. Peyré, · 2014
Cited alongside, same era.
“Deep convolutional neural network for image deconvolution,”
L. Xu, J.S.J. Ren, C. Liu, and J. Jia, · 2014
Cited alongside, same era.
“A+: Adjusted anchored neighborhood regression for fast super-resolution,”
R. Timofte, V. De Smet, and L. Van Gool, · 2014
Cited alongside, same era.
“A nonlocal structure tensor-based approach for multicomponent image recovery problems,”
G. Chierchia, N. Pustelnik, B. Pesquet-Popescu, and J.-C. Pesquet, · 2014
Cited alongside, same era.
“Learning a deep convolutional network for image super-resolution,”
C. Dong, C. L. Chen, K. He, and X. Tang, · 2014
Cited alongside, same era.
A. Lucas, M. Iliadis, R. Molina, and A.K. Katsaggelos, · 2018
Later among the works it cites.
“Deep convolutional framelets: A general deep learning framework for inverse problems,”
J.C. Ye, Y. Han, and E. Cha, · 2018
Later among the works it cites.
“Learned primal-dual reconstruction,”
J. Adler and O. Oktem, · 2018
Later among the works it cites.
“Nonsmooth convex optimization for structured illumination microscopy image reconstruction,”
J. Boulanger, N. Pustelnik, L. Condat, L. Sengmanivong, and T. Piolot, · 2018
Later among the works it cites.
“Multi-level wavelet-CNN for image restoration,”
P. Liu, H. Zhang, K. Zhang, L. Lin, and W. Zuo, · 2018
Later among the works it cites.
“Proximal splitting algorithms: Relax them all!,”
L. Condat, D. Kitahara, A. Contreras, and A. Hirabayashi, · 2019
Later among the works it cites.
“A parallel and automatically tuned algorithm for multispectral image deconvolution,”
R. Ammanouil, A. Ferrari, D. Mary, C. Ferrari, and F. Loi, · 2019
Later among the works it cites.
“Deep convolutional framelets: A general deep learning framework for inverse problems,”
S. Ravishankar, J.C. Ye, and J.A. Fessler, · 2019
Later among the works it cites.
“Learning the invisible: A hybrid deep learning-shearlet framework for limited angle computed tomography,”
T. A. Bubba, G. Kutyniok, M. Lassas, M. M’́arz, W. Samek, S. Siltanen, and V. Srinivasan, · 2019
Later among the works it cites.
“Neumann networks for linear inverse problems in imaging,”
D. Gilton, G. Ongie, and R. Willett, · 2019
Later among the works it cites.
“Simultaneous fidelity and regularization learning for image restoration,”
D. Ren, W. Zuo, D. Zhang, L. Zhang, and M. Yang, · 2019
Later among the works it cites.
B. Pascal, S. Vaiter, N. Pustelnik, and P. Abry, · 2020
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