Fetching the paper…
Reading the bibliography…
While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks.
A simple weight decay can improve generalization
A. Krogh and J. A. Hertz · 1992
Earlier work this paper cites.
Nonlinear total variation based noise removal algorithms
L. I. Rudin, S. Osher, and E. Fatemi · 1992
Earlier work this paper cites.
Video denoising by sparse 3d transform-domain collaborative filtering
K. Dabov, A. Foi, and K. Egiazarian · 2007
Earlier work this paper cites.
Deconvolution using natural image priors
A. Levin, R. Fergus, F. Durand, and W. T. Freeman · 2007
Earlier work this paper cites.
Image restoration by sparse 3d transform-domain collaborative filtering
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian · 2008
Earlier work this paper cites.
Fast image deconvolution using hyper-laplacian priors
D. Krishnan and R. Fergus · 2009
Earlier work this paper cites.
An algorithm for minimizing the piecewise smooth Mumford-Shah functional
T. Pock, D. Cremers, H. Bischof, and A. Chambolle · 2009
Earlier work this paper cites.
Fields of experts
S. Roth and M. J. Black · 2009
Earlier work this paper cites.
A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science
E. Esser, X. Zhang, and T. Chan · 2010
Earlier work this paper cites.
Non-Local Means Denoising
A. Buades, B. Coll, and J.-M. Morel · 2011
Earlier work this paper cites.
A first-order primal-dual algorithm for convex problems with applications to imaging
A. Chambolle and T. Pock · 2011
Earlier work this paper cites.
Color Demosaicking by Local Directional Interpolation and Nonlocal Adaptive Thresholding
L. Zhang, X. Wu, A. Buades, and X. Li · 2011
Earlier work this paper cites.
From learning models of natural image patches to whole image restoration
D. Zoran and Y. Weiss · 2011
Earlier work this paper cites.
Bm3d frames and variational image deblurring
A. Danielyan, V. Katkovnik, and K. Egiazarian · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Image denoising and inpainting with deep neural networks
J. Xie, L. Xu, and E. Chen · 2012
Earlier work this paper cites.
A machine learning approach for non-blind image deconvolution
C. J. Schuler, H. C. Burger, S. Harmeling, and B. Scholkopf · 2013
Earlier work this paper cites.
Plug-and-Play Priors for Model Based Reconstruction
S. V. Venkatakrishnan, C. A. Bouman, and B. Wohlberg · 2013
Cited alongside, same era.
Learning a deep convolutional network for image super-resolution
C. Dong, C. C. Loy, K. He, and X. Tang · 2014
Cited alongside, same era.
Flexisp: A flexible camera image processing framework
F. Heide, M. Steinberger, Y.-T. Tsai, M. Rouf, D. Pająk, D. Reddy, O. Gallo, J. L. abd Wolfgang Heidrich, K. Egiazarian, J. Kautz, and K. Pulli · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Mask-specific inpainting with deep neural networks
R. Köhler, C. Schuler, B. Schölkopf, and S. Harmeling · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
ProxImaL: Efficient Image Optimization using Proximal Algorithms
H. F., D. S., N. M., R.-K. J., and W. G. Heidrich W · 2016
Later among the works it cites.
Deep joint demosaicking and denoising
M. Gharbi, G. Chaurasia, S. Paris, and F. Durand · 2016
Later among the works it cites.
Deep discrete flow
F. Güney and A. Geiger · 2016
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Learning joint demosaicing and denoising based on sequential energy minimization
T. Klatzer, K. Hammernik, P. Knobelreiter, and T. Pock · 2016
Later among the works it cites.
Efficient deep learning for stereo matching
W. Luo, A. G. Schwing, and R. Urtasun · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A multilayer neural network for image demosaicking
Y. Q. Wang · 2014
Cited alongside, same era.
Deep convolutional neural network for image deconvolution
L. Xu, J. S. Ren, C. Liu, and J. Jia · 2014
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Cited alongside, same era.
A deep visual correspondence embedding model for stereo matching costs
Z. Chen, X. Sun, L. Wang, Y. Yu, and C. Huang · 2015
Cited alongside, same era.
FlowNet: Learning Optical Flow with Convolutional Networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
Later among the works it cites.
Poisson inverse problems by the plug-and-play scheme
A. Rond, R. Giryes, and M. Elad · 2016
Later among the works it cites.
Plug-and-play priors for bright field electron tomography and sparse interpolation
S. Sreehari, S. V. Venkatakrishnan, B. Wohlberg, G. T. Buzzard, L. F. Drummy, J. P. Simmons, and C. A. Bouman · 2016
Later among the works it cites.
Image restoration and reconstruction using variable splitting and class-adapted image priors
A. M. Teodoro, J. M. Bioucas-Dias, and M. A. T. Figueiredo · 2016
Later among the works it cites.
Proximal deep structured models
S. Wang, S. Fidler, and R. Urtasun · 2016
Later among the works it cites.
Semantic image inpainting with perceptual and contextual losses
R. Yeh, C. Chen, T. Lim, M. Hasegawa-Johnson, and M. N. Do · 2016
Later among the works it cites.
Stereo matching by training a convolutional neural network to compare image patches
J. Zbontar and Y. LeCun · 2016
Later among the works it cites.
Plug-and-play admm for image restoration: Fixed-point convergence and applications
S. H. Chan, X. Wang, and O. A. Elgendy · 2017
Closest in time.
Sharpening hyperspectral images using plug-and-play priors
A. Teodoro, J. Bioucas-Dias, and M. Figueiredo · 2017
Closest in time.
Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang · 2017
Closest in time.