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Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks.
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 super-resolution as sparse representation of raw image patches
J. Yang, J. Wright, T. Huang, and Y. Ma · 2008
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Natural image denoising with convolutional networks
V. Jain and S. Seung · 2009
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Large-margin classification in infinite neural networks
Y. Cho and L. K. Saul · 2010
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
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.
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
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A machine learning approach for non-blind image deconvolution
C. J. Schuler, H. Christopher Burger, S. Harmeling, and B. Scholkopf · 2013
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Learning activation functions to improve deep neural networks
F. Agostinelli, M. Hoffman, P. Sadowski, and P. Baldi · 2014
Earlier work this paper cites.
Weighted nuclear norm minimization with application to image denoising
S. Gu, L. Zhang, W. Zuo, and X. Feng · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Shrinkage fields for effective image restoration
U. Schmidt and S. Roth · 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.
Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
Cited alongside, same era.
Convolutional sparse coding for image super-resolution
S. Gu, W. Zuo, Q. Xie, D. Meng, X. Feng, and L. Zhang · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Image super-resolution using deep convolutional networks
Soft-to-hard vector quantization for end-to-end learning compressible representations
E. Agustsson, F. Mentzer, M. Tschannen, L. Cavigelli, R. Timofte, L. Benini, and L. V. Gool · 2017
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Ntire 2017 challenge on single image super-resolution: Dataset and study
E. Agustsson and R. Timofte · 2017
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Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration
Y. Chen and T. Pock · 2017
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J.-S. Choi and M. Kim · 2017
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Weighted nuclear norm minimization and its applications to low level vision
S. Gu, Q. Xie, D. Meng, W. Zuo, X. Feng, and L. Zhang · 2017
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C. Dong, C. C. Loy, K. He, and X. Tang · 2016
Cited alongside, same era.
Accelerating the super-resolution convolutional neural network
C. Dong, C. C. Loy, and X. Tang · 2016
Cited alongside, same era.
Binarized neural networks
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
Cited alongside, same era.
Accurate image super-resolution using very deep convolutional networks
J. Kim, J. Kwon Lee, and K. Mu Lee · 2016
Cited alongside, same era.
Multi-bias non-linear activation in deep neural networks
H. Li, W. Ouyang, and X. Wang · 2016
Cited alongside, same era.
Deep laplacian pyramid networks for fast and accurate superresolution
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
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Enhanced deep residual networks for single image super-resolution
B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee · 2017
Later among the works it cites.
Memnet: A persistent memory network for image restoration
Y. Tai, J. Yang, X. Liu, and C. Xu · 2017
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Lossy image compression with compressive autoencoders
L. Theis, W. Shi, A. Cunningham, and F. Huszár · 2017
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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.
Learning deep cnn denoiser prior for image restoration
K. Zhang, W. Zuo, S. Gu, and L. Zhang · 2017
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Ffdnet: Toward a fast and flexible solution for cnn based image denoising
K. Zhang, W. Zuo, and L. Zhang · 2017
Later among the works it cites.